{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.7"
    },
    "colab": {
      "name": "6-4_EfficientGAN.ipynb",
      "provenance": [],
      "collapsed_sections": []
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7ZaLsEIeL8J7",
        "colab_type": "text"
      },
      "source": [
        "# 6.4 Efficient GANの作成\n",
        "\n",
        "- 本ファイルでは、Efficient GANのネットワークを実装し、学習をします。\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ey6RtLORL8KI",
        "colab_type": "text"
      },
      "source": [
        "# 6.4 学習目標\n",
        "\n",
        "1.\tEfficient GANを実装し、手書き数字画像で異常検知が生成できる"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LNnV0rM1L8KK",
        "colab_type": "text"
      },
      "source": [
        "# 事前準備\n",
        "書籍の指示に従い、本章で使用するデータを用意します"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_lGX1rk4L8KL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# パッケージのimport\n",
        "import random\n",
        "import math\n",
        "import time\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "\n",
        "import torch\n",
        "import torch.utils.data as data\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "\n",
        "from torchvision import transforms"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EKc25TAjL8KP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Setup seeds\n",
        "torch.manual_seed(1234)\n",
        "torch.cuda.manual_seed(1234)\n",
        "np.random.seed(1234)\n",
        "random.seed(1234)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vlfVYAe2L8KR",
        "colab_type": "text"
      },
      "source": [
        "# Generatorの実装\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "68sd3hevL8KS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Generator(nn.Module):\n",
        "\n",
        "    def __init__(self, z_dim=20):\n",
        "        super(Generator, self).__init__()\n",
        "\n",
        "        self.layer1 = nn.Sequential(\n",
        "            nn.Linear(z_dim, 1024),\n",
        "            nn.BatchNorm1d(1024),\n",
        "            nn.ReLU(inplace=True))\n",
        "\n",
        "        self.layer2 = nn.Sequential(\n",
        "            nn.Linear(1024, 7*7*128),\n",
        "            nn.BatchNorm1d(7*7*128),\n",
        "            nn.ReLU(inplace=True))\n",
        "\n",
        "        self.layer3 = nn.Sequential(\n",
        "            nn.ConvTranspose2d(in_channels=128, out_channels=64,\n",
        "                               kernel_size=4, stride=2, padding=1),\n",
        "            nn.BatchNorm2d(64),\n",
        "            nn.ReLU(inplace=True))\n",
        "\n",
        "        self.last = nn.Sequential(\n",
        "            nn.ConvTranspose2d(in_channels=64, out_channels=1,\n",
        "                               kernel_size=4, stride=2, padding=1),\n",
        "            nn.Tanh())\n",
        "        # 注意：白黒画像なので出力チャネルは1つだけ\n",
        "\n",
        "    def forward(self, z):\n",
        "        out = self.layer1(z)\n",
        "        out = self.layer2(out)\n",
        "\n",
        "        # 転置畳み込み層に入れるためにテンソルの形を整形\n",
        "        out = out.view(z.shape[0], 128, 7, 7)\n",
        "        out = self.layer3(out)\n",
        "        out = self.last(out)\n",
        "\n",
        "        return out\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zWygUNjSL8KU",
        "colab_type": "code",
        "colab": {},
        "outputId": "71e4bf9e-abf8-497f-f265-910f83e7d4ae"
      },
      "source": [
        "# 動作確認\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "G = Generator(z_dim=20)\n",
        "G.train()\n",
        "\n",
        "# 入力する乱数\n",
        "# バッチノーマライゼーションがあるのでミニバッチ数は2以上\n",
        "input_z = torch.randn(2, 20)\n",
        "\n",
        "# 偽画像を出力\n",
        "fake_images = G(input_z)  # torch.Size([2, 1, 28, 28])\n",
        "img_transformed = fake_images[0][0].detach().numpy()\n",
        "plt.imshow(img_transformed, 'gray')\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YIyWxLJPL8KW",
        "colab_type": "text"
      },
      "source": [
        "# Discriminatorの実装"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ug0Iof6uL8KX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Discriminator(nn.Module):\n",
        "\n",
        "    def __init__(self, z_dim=20):\n",
        "        super(Discriminator, self).__init__()\n",
        "\n",
        "        # 画像側の入力処理\n",
        "        self.x_layer1 = nn.Sequential(\n",
        "            nn.Conv2d(1, 64, kernel_size=4,\n",
        "                      stride=2, padding=1),\n",
        "            nn.LeakyReLU(0.1, inplace=True))\n",
        "        # 注意：白黒画像なので入力チャネルは1つだけ\n",
        "\n",
        "        self.x_layer2 = nn.Sequential(\n",
        "            nn.Conv2d(64, 64, kernel_size=4,\n",
        "                      stride=2, padding=1),\n",
        "            nn.BatchNorm2d(64),\n",
        "            nn.LeakyReLU(0.1, inplace=True))\n",
        "\n",
        "        # 乱数側の入力処理\n",
        "        self.z_layer1 = nn.Linear(z_dim, 512)\n",
        "\n",
        "        # 最後の判定\n",
        "        self.last1 = nn.Sequential(\n",
        "            nn.Linear(3648, 1024),\n",
        "            nn.LeakyReLU(0.1, inplace=True))\n",
        "\n",
        "        self.last2 = nn.Linear(1024, 1)\n",
        "\n",
        "    def forward(self, x, z):\n",
        "\n",
        "        # 画像側の入力処理\n",
        "        x_out = self.x_layer1(x)\n",
        "        x_out = self.x_layer2(x_out)\n",
        "\n",
        "        # 乱数側の入力処理\n",
        "        z = z.view(z.shape[0], -1)\n",
        "        z_out = self.z_layer1(z)\n",
        "\n",
        "        # x_outとz_outを結合し、全結合層で判定\n",
        "        x_out = x_out.view(-1, 64 * 7 * 7)\n",
        "        out = torch.cat([x_out, z_out], dim=1)\n",
        "        out = self.last1(out)\n",
        "\n",
        "        feature = out  # 最後にチャネルを1つに集約する手前の情報\n",
        "        feature = feature.view(feature.size()[0], -1)  # 2次元に変換\n",
        "\n",
        "        out = self.last2(out)\n",
        "\n",
        "        return out, feature\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fsKNLk4BL8Ka",
        "colab_type": "code",
        "colab": {},
        "outputId": "072f54ca-2b19-486c-ef56-1ad9f2f24a91"
      },
      "source": [
        "# 動作確認\n",
        "D = Discriminator(z_dim=20)\n",
        "\n",
        "# 偽画像を生成\n",
        "input_z = torch.randn(2, 20)\n",
        "fake_images = G(input_z)\n",
        "\n",
        "# 偽画像をDに入力\n",
        "d_out, _ = D(fake_images, input_z)\n",
        "\n",
        "# 出力d_outにSigmoidをかけて0から1に変換\n",
        "print(nn.Sigmoid()(d_out))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[0.4976],\n",
            "        [0.4939]], grad_fn=<SigmoidBackward>)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YlRmvebQL8Kd",
        "colab_type": "text"
      },
      "source": [
        "# Encoderの実装\n",
        "\n",
        "画像をzに変換する"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ih6gWabsL8Kd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Encoder(nn.Module):\n",
        "\n",
        "    def __init__(self, z_dim=20):\n",
        "        super(Encoder, self).__init__()\n",
        "\n",
        "        self.layer1 = nn.Sequential(\n",
        "            nn.Conv2d(1, 32, kernel_size=3,\n",
        "                      stride=1),\n",
        "            nn.LeakyReLU(0.1, inplace=True))\n",
        "        # 注意：白黒画像なので入力チャネルは1つだけ\n",
        "\n",
        "        self.layer2 = nn.Sequential(\n",
        "            nn.Conv2d(32, 64, kernel_size=3,\n",
        "                      stride=2, padding=1),\n",
        "            nn.BatchNorm2d(64),\n",
        "            nn.LeakyReLU(0.1, inplace=True))\n",
        "\n",
        "        self.layer3 = nn.Sequential(\n",
        "            nn.Conv2d(64, 128, kernel_size=3,\n",
        "                      stride=2, padding=1),\n",
        "            nn.BatchNorm2d(128),\n",
        "            nn.LeakyReLU(0.1, inplace=True))\n",
        "\n",
        "        # ここまでで画像のサイズは7×7になっている\n",
        "        self.last = nn.Linear(128 * 7 * 7, z_dim)\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = self.layer1(x)\n",
        "        out = self.layer2(out)\n",
        "        out = self.layer3(out)\n",
        "\n",
        "        # FCに入れるためにテンソルの形を整形\n",
        "        out = out.view(-1, 128 * 7 * 7)\n",
        "        out = self.last(out)\n",
        "\n",
        "        return out\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vun_KvShL8Kh",
        "colab_type": "code",
        "colab": {},
        "outputId": "7234536c-2ce6-4c4e-e59b-b2ff071c6e7b"
      },
      "source": [
        "# 動作確認\n",
        "E = Encoder(z_dim=20)\n",
        "\n",
        "# 入力する画像データ\n",
        "x = fake_images  # fake_imagesは上のGで作成したもの\n",
        "\n",
        "# 画像からzをEncode\n",
        "z = E(x)\n",
        "\n",
        "print(z.shape)\n",
        "print(z)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([2, 20])\n",
            "tensor([[-0.2117, -0.3586,  0.1473, -0.1527, -0.0279, -0.2314, -0.0856, -0.2432,\n",
            "          0.0342,  0.1805,  0.0172, -0.0165,  0.1778,  0.3555,  0.2951, -0.3447,\n",
            "         -0.3518, -0.1325, -0.0354, -0.2111],\n",
            "        [-0.1011,  0.2350,  0.0888, -0.2578,  0.1728,  0.3983,  0.1063, -0.1835,\n",
            "         -0.0881,  0.1863,  0.2127, -0.1049,  0.0425,  0.0202,  0.2042, -0.2098,\n",
            "          0.1348, -0.1092, -0.3779, -0.2373]], grad_fn=<AddmmBackward>)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BnwXk0a-L8Kl",
        "colab_type": "text"
      },
      "source": [
        "# DataLoaderの作成"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8lXhlnQKL8Kl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def make_datapath_list():\n",
        "    \"\"\"学習、検証の画像データとアノテーションデータへのファイルパスリストを作成する。 \"\"\"\n",
        "\n",
        "    train_img_list = list()  # 画像ファイルパスを格納\n",
        "\n",
        "    for img_idx in range(200):\n",
        "        img_path = \"./data/img_78_28size/img_7_\" + str(img_idx)+'.jpg'\n",
        "        train_img_list.append(img_path)\n",
        "\n",
        "        img_path = \"./data/img_78_28size/img_8_\" + str(img_idx)+'.jpg'\n",
        "        train_img_list.append(img_path)\n",
        "\n",
        "    return train_img_list\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ffLPO9nJL8Kn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class ImageTransform():\n",
        "    \"\"\"画像の前処理クラス\"\"\"\n",
        "\n",
        "    def __init__(self, mean, std):\n",
        "        self.data_transform = transforms.Compose([\n",
        "            transforms.ToTensor(),\n",
        "            transforms.Normalize(mean, std)\n",
        "        ])\n",
        "\n",
        "    def __call__(self, img):\n",
        "        return self.data_transform(img)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pc4oS_UML8Kq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class GAN_Img_Dataset(data.Dataset):\n",
        "    \"\"\"画像のDatasetクラス。PyTorchのDatasetクラスを継承\"\"\"\n",
        "\n",
        "    def __init__(self, file_list, transform):\n",
        "        self.file_list = file_list\n",
        "        self.transform = transform\n",
        "\n",
        "    def __len__(self):\n",
        "        '''画像の枚数を返す'''\n",
        "        return len(self.file_list)\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        '''前処理をした画像のTensor形式のデータを取得'''\n",
        "\n",
        "        img_path = self.file_list[index]\n",
        "        img = Image.open(img_path)  # [高さ][幅]白黒\n",
        "\n",
        "        # 画像の前処理\n",
        "        img_transformed = self.transform(img)\n",
        "\n",
        "        return img_transformed\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IfBEgPZUL8Ks",
        "colab_type": "code",
        "colab": {},
        "outputId": "15bf560c-7348-455d-ffa3-20dd8f01f18b"
      },
      "source": [
        "# DataLoaderの作成と動作確認\n",
        "\n",
        "# ファイルリストを作成\n",
        "train_img_list=make_datapath_list()\n",
        "\n",
        "# Datasetを作成\n",
        "mean = (0.5,)\n",
        "std = (0.5,)\n",
        "train_dataset = GAN_Img_Dataset(\n",
        "    file_list=train_img_list, transform=ImageTransform(mean, std))\n",
        "\n",
        "# DataLoaderを作成\n",
        "batch_size = 64\n",
        "\n",
        "train_dataloader = torch.utils.data.DataLoader(\n",
        "    train_dataset, batch_size=batch_size, shuffle=True)\n",
        "\n",
        "# 動作の確認\n",
        "batch_iterator = iter(train_dataloader)  # イテレータに変換\n",
        "imges = next(batch_iterator)  # 1番目の要素を取り出す\n",
        "print(imges.size())  # torch.Size([64, 1, 64, 64])\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([64, 1, 28, 28])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j9vLW5T6L8Ku",
        "colab_type": "text"
      },
      "source": [
        "# 学習させる"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SDF9_QozL8Kv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# モデルを学習させる関数を作成\n",
        "\n",
        "\n",
        "def train_model(G, D, E, dataloader, num_epochs):\n",
        "\n",
        "    # GPUが使えるかを確認\n",
        "    device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "    print(\"使用デバイス：\", device)\n",
        "\n",
        "    # 最適化手法の設定\n",
        "    lr_ge = 0.0001\n",
        "    lr_d = 0.0001/4\n",
        "    beta1, beta2 = 0.5, 0.999\n",
        "    g_optimizer = torch.optim.Adam(G.parameters(), lr_ge, [beta1, beta2])\n",
        "    e_optimizer = torch.optim.Adam(E.parameters(), lr_ge, [beta1, beta2])\n",
        "    d_optimizer = torch.optim.Adam(D.parameters(), lr_d, [beta1, beta2])\n",
        "\n",
        "    # 誤差関数を定義\n",
        "    # BCEWithLogitsLossは入力にシグモイド（logit）をかけてから、\n",
        "    # バイナリークロスエントロピーを計算\n",
        "    criterion = nn.BCEWithLogitsLoss(reduction='mean')\n",
        "\n",
        "    # パラメータをハードコーディング\n",
        "    z_dim = 20\n",
        "    mini_batch_size = 64\n",
        "\n",
        "    # ネットワークをGPUへ\n",
        "    G.to(device)\n",
        "    E.to(device)\n",
        "    D.to(device)\n",
        "\n",
        "    G.train()  # モデルを訓練モードに\n",
        "    E.train()  # モデルを訓練モードに\n",
        "    D.train()  # モデルを訓練モードに\n",
        "\n",
        "    # ネットワークがある程度固定であれば、高速化させる\n",
        "    torch.backends.cudnn.benchmark = True\n",
        "\n",
        "    # 画像の枚数\n",
        "    num_train_imgs = len(dataloader.dataset)\n",
        "    batch_size = dataloader.batch_size\n",
        "\n",
        "    # イテレーションカウンタをセット\n",
        "    iteration = 1\n",
        "    logs = []\n",
        "\n",
        "    # epochのループ\n",
        "    for epoch in range(num_epochs):\n",
        "\n",
        "        # 開始時刻を保存\n",
        "        t_epoch_start = time.time()\n",
        "        epoch_g_loss = 0.0  # epochの損失和\n",
        "        epoch_e_loss = 0.0  # epochの損失和\n",
        "        epoch_d_loss = 0.0  # epochの損失和\n",
        "\n",
        "        print('-------------')\n",
        "        print('Epoch {}/{}'.format(epoch, num_epochs))\n",
        "        print('-------------')\n",
        "        print('（train）')\n",
        "\n",
        "        # データローダーからminibatchずつ取り出すループ\n",
        "        for imges in dataloader:\n",
        "\n",
        "            # ミニバッチがサイズが1だと、バッチノーマライゼーションでエラーになるのでさける\n",
        "            if imges.size()[0] == 1:\n",
        "                continue\n",
        "\n",
        "            # ミニバッチサイズの1もしくは0のラベル役のテンソルを作成\n",
        "            # 正解ラベルと偽ラベルを作成\n",
        "            # epochの最後のイテレーションはミニバッチの数が少なくなる\n",
        "            mini_batch_size = imges.size()[0]\n",
        "            label_real = torch.full((mini_batch_size,), 1).to(device)\n",
        "            label_fake = torch.full((mini_batch_size,), 0).to(device)\n",
        "\n",
        "            # GPUが使えるならGPUにデータを送る\n",
        "            imges = imges.to(device)\n",
        "\n",
        "            # --------------------\n",
        "            # 1. Discriminatorの学習\n",
        "            # --------------------\n",
        "            # 真の画像を判定　\n",
        "            z_out_real = E(imges)\n",
        "            d_out_real, _ = D(imges, z_out_real)\n",
        "\n",
        "            # 偽の画像を生成して判定\n",
        "            input_z = torch.randn(mini_batch_size, z_dim).to(device)\n",
        "            fake_images = G(input_z)\n",
        "            d_out_fake, _ = D(fake_images, input_z)\n",
        "\n",
        "            # 誤差を計算\n",
        "            d_loss_real = criterion(d_out_real.view(-1), label_real)\n",
        "            d_loss_fake = criterion(d_out_fake.view(-1), label_fake)\n",
        "            d_loss = d_loss_real + d_loss_fake\n",
        "\n",
        "            # バックプロパゲーション\n",
        "            d_optimizer.zero_grad()\n",
        "            d_loss.backward()\n",
        "            d_optimizer.step()\n",
        "\n",
        "            # --------------------\n",
        "            # 2. Generatorの学習\n",
        "            # --------------------\n",
        "            # 偽の画像を生成して判定\n",
        "            input_z = torch.randn(mini_batch_size, z_dim).to(device)\n",
        "            fake_images = G(input_z)\n",
        "            d_out_fake, _ = D(fake_images, input_z)\n",
        "\n",
        "            # 誤差を計算\n",
        "            g_loss = criterion(d_out_fake.view(-1), label_real)\n",
        "\n",
        "            # バックプロパゲーション\n",
        "            g_optimizer.zero_grad()\n",
        "            g_loss.backward()\n",
        "            g_optimizer.step()\n",
        "\n",
        "            # --------------------\n",
        "            # 3. Encoderの学習\n",
        "            # --------------------\n",
        "            # 真の画像のzを推定\n",
        "            z_out_real = E(imges)\n",
        "            d_out_real, _ = D(imges, z_out_real)\n",
        "\n",
        "            # 誤差を計算\n",
        "            e_loss = criterion(d_out_real.view(-1), label_fake)\n",
        "\n",
        "            # バックプロパゲーション\n",
        "            e_optimizer.zero_grad()\n",
        "            e_loss.backward()\n",
        "            e_optimizer.step()\n",
        "\n",
        "            # --------------------\n",
        "            # 4. 記録\n",
        "            # --------------------\n",
        "            epoch_d_loss += d_loss.item()\n",
        "            epoch_g_loss += g_loss.item()\n",
        "            epoch_e_loss += e_loss.item()\n",
        "            iteration += 1\n",
        "\n",
        "        # epochのphaseごとのlossと正解率\n",
        "        t_epoch_finish = time.time()\n",
        "        print('-------------')\n",
        "        print('epoch {} || Epoch_D_Loss:{:.4f} ||Epoch_G_Loss:{:.4f} ||Epoch_E_Loss:{:.4f}'.format(\n",
        "            epoch, epoch_d_loss/batch_size, epoch_g_loss/batch_size, epoch_e_loss/batch_size))\n",
        "        print('timer:  {:.4f} sec.'.format(t_epoch_finish - t_epoch_start))\n",
        "        t_epoch_start = time.time()\n",
        "\n",
        "    print(\"総イテレーション回数:\", iteration)\n",
        "\n",
        "    return G, D, E\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3o7-C9d7L8Kz",
        "colab_type": "code",
        "colab": {},
        "outputId": "a0cdf3bd-089a-4879-f1d9-06968f870aa8"
      },
      "source": [
        "# ネットワークの初期化\n",
        "def weights_init(m):\n",
        "    classname = m.__class__.__name__\n",
        "    if classname.find('Conv') != -1:\n",
        "        # conv2dとConvTranspose2dの初期化\n",
        "        nn.init.normal_(m.weight.data, 0.0, 0.02)\n",
        "        nn.init.constant_(m.bias.data, 0)\n",
        "    elif classname.find('BatchNorm') != -1:\n",
        "        # BatchNorm2dの初期化\n",
        "        nn.init.normal_(m.weight.data, 0.0, 0.02)\n",
        "        nn.init.constant_(m.bias.data, 0)\n",
        "    elif classname.find('Linear') != -1:\n",
        "        # 全結合層Linearの初期化\n",
        "        m.bias.data.fill_(0)\n",
        "\n",
        "\n",
        "# 初期化の実施\n",
        "G.apply(weights_init)\n",
        "E.apply(weights_init)\n",
        "D.apply(weights_init)\n",
        "\n",
        "print(\"ネットワークの初期化完了\")\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ネットワークの初期化完了\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0-eQCLPJL8K1",
        "colab_type": "code",
        "colab": {},
        "outputId": "8b3e2089-8917-4987-a653-f0e760c3ad2d"
      },
      "source": [
        "# 学習・検証を実行する\n",
        "# 15分ほどかかる\n",
        "num_epochs = 1500\n",
        "G_update, D_update, E_update = train_model(\n",
        "    G, D, E, dataloader=train_dataloader, num_epochs=num_epochs)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "使用デバイス： cuda:0\n",
            "-------------\n",
            "Epoch 0/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 0 || Epoch_D_Loss:0.1490 ||Epoch_G_Loss:0.0781 ||Epoch_E_Loss:0.0768\n",
            "timer:  0.9127 sec.\n",
            "-------------\n",
            "Epoch 1/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1 || Epoch_D_Loss:0.1451 ||Epoch_G_Loss:0.0805 ||Epoch_E_Loss:0.0785\n",
            "timer:  0.4236 sec.\n",
            "-------------\n",
            "Epoch 2/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 2 || Epoch_D_Loss:0.1407 ||Epoch_G_Loss:0.0823 ||Epoch_E_Loss:0.0809\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 3/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 3 || Epoch_D_Loss:0.1365 ||Epoch_G_Loss:0.0848 ||Epoch_E_Loss:0.0841\n",
            "timer:  0.4250 sec.\n",
            "-------------\n",
            "Epoch 4/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 4 || Epoch_D_Loss:0.1331 ||Epoch_G_Loss:0.0863 ||Epoch_E_Loss:0.0868\n",
            "timer:  0.4236 sec.\n",
            "-------------\n",
            "Epoch 5/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 5 || Epoch_D_Loss:0.1294 ||Epoch_G_Loss:0.0877 ||Epoch_E_Loss:0.0890\n",
            "timer:  0.4248 sec.\n",
            "-------------\n",
            "Epoch 6/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 6 || Epoch_D_Loss:0.1277 ||Epoch_G_Loss:0.0897 ||Epoch_E_Loss:0.0911\n",
            "timer:  0.4252 sec.\n",
            "-------------\n",
            "Epoch 7/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 7 || Epoch_D_Loss:0.1242 ||Epoch_G_Loss:0.0909 ||Epoch_E_Loss:0.0942\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 8/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 8 || Epoch_D_Loss:0.1201 ||Epoch_G_Loss:0.0930 ||Epoch_E_Loss:0.0981\n",
            "timer:  0.4242 sec.\n",
            "-------------\n",
            "Epoch 9/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 9 || Epoch_D_Loss:0.1180 ||Epoch_G_Loss:0.0953 ||Epoch_E_Loss:0.1003\n",
            "timer:  0.4230 sec.\n",
            "-------------\n",
            "Epoch 10/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 10 || Epoch_D_Loss:0.1154 ||Epoch_G_Loss:0.0970 ||Epoch_E_Loss:0.1028\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 11/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 11 || Epoch_D_Loss:0.1110 ||Epoch_G_Loss:0.0983 ||Epoch_E_Loss:0.1079\n",
            "timer:  0.4241 sec.\n",
            "-------------\n",
            "Epoch 12/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 12 || Epoch_D_Loss:0.1082 ||Epoch_G_Loss:0.1009 ||Epoch_E_Loss:0.1092\n",
            "timer:  0.4236 sec.\n",
            "-------------\n",
            "Epoch 13/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 13 || Epoch_D_Loss:0.1023 ||Epoch_G_Loss:0.1039 ||Epoch_E_Loss:0.1182\n",
            "timer:  0.4298 sec.\n",
            "-------------\n",
            "Epoch 14/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 14 || Epoch_D_Loss:0.0956 ||Epoch_G_Loss:0.1076 ||Epoch_E_Loss:0.1250\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 15/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 15 || Epoch_D_Loss:0.0932 ||Epoch_G_Loss:0.1114 ||Epoch_E_Loss:0.1267\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 16/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 16 || Epoch_D_Loss:0.0882 ||Epoch_G_Loss:0.1177 ||Epoch_E_Loss:0.1346\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 17/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 17 || Epoch_D_Loss:0.0804 ||Epoch_G_Loss:0.1235 ||Epoch_E_Loss:0.1446\n",
            "timer:  0.4250 sec.\n",
            "-------------\n",
            "Epoch 18/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 18 || Epoch_D_Loss:0.0760 ||Epoch_G_Loss:0.1275 ||Epoch_E_Loss:0.1506\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 19/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 19 || Epoch_D_Loss:0.0733 ||Epoch_G_Loss:0.1338 ||Epoch_E_Loss:0.1517\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 20/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 20 || Epoch_D_Loss:0.0701 ||Epoch_G_Loss:0.1404 ||Epoch_E_Loss:0.1543\n",
            "timer:  0.4251 sec.\n",
            "-------------\n",
            "Epoch 21/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 21 || Epoch_D_Loss:0.0611 ||Epoch_G_Loss:0.1476 ||Epoch_E_Loss:0.1739\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 22/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 22 || Epoch_D_Loss:0.0548 ||Epoch_G_Loss:0.1552 ||Epoch_E_Loss:0.1868\n",
            "timer:  0.4301 sec.\n",
            "-------------\n",
            "Epoch 23/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 23 || Epoch_D_Loss:0.0510 ||Epoch_G_Loss:0.1637 ||Epoch_E_Loss:0.1912\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 24/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 24 || Epoch_D_Loss:0.0483 ||Epoch_G_Loss:0.1723 ||Epoch_E_Loss:0.1959\n",
            "timer:  0.4250 sec.\n",
            "-------------\n",
            "Epoch 25/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 25 || Epoch_D_Loss:0.0447 ||Epoch_G_Loss:0.1813 ||Epoch_E_Loss:0.2017\n",
            "timer:  0.4244 sec.\n",
            "-------------\n",
            "Epoch 26/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 26 || Epoch_D_Loss:0.0433 ||Epoch_G_Loss:0.1870 ||Epoch_E_Loss:0.2037\n",
            "timer:  0.4243 sec.\n",
            "-------------\n",
            "Epoch 27/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 27 || Epoch_D_Loss:0.0399 ||Epoch_G_Loss:0.1969 ||Epoch_E_Loss:0.2131\n",
            "timer:  0.4254 sec.\n",
            "-------------\n",
            "Epoch 28/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 28 || Epoch_D_Loss:0.0351 ||Epoch_G_Loss:0.2054 ||Epoch_E_Loss:0.2360\n",
            "timer:  0.4250 sec.\n",
            "-------------\n",
            "Epoch 29/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 29 || Epoch_D_Loss:0.0311 ||Epoch_G_Loss:0.2168 ||Epoch_E_Loss:0.2512\n",
            "timer:  0.4255 sec.\n",
            "-------------\n",
            "Epoch 30/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 30 || Epoch_D_Loss:0.0299 ||Epoch_G_Loss:0.2217 ||Epoch_E_Loss:0.2529\n",
            "timer:  0.4279 sec.\n",
            "-------------\n",
            "Epoch 31/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 31 || Epoch_D_Loss:0.0294 ||Epoch_G_Loss:0.2326 ||Epoch_E_Loss:0.2540\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 32/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 32 || Epoch_D_Loss:0.0282 ||Epoch_G_Loss:0.2355 ||Epoch_E_Loss:0.2525\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 33/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 33 || Epoch_D_Loss:0.0261 ||Epoch_G_Loss:0.2476 ||Epoch_E_Loss:0.2618\n",
            "timer:  0.4247 sec.\n",
            "-------------\n",
            "Epoch 34/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 34 || Epoch_D_Loss:0.0218 ||Epoch_G_Loss:0.2554 ||Epoch_E_Loss:0.2926\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 35/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 35 || Epoch_D_Loss:0.0193 ||Epoch_G_Loss:0.2678 ||Epoch_E_Loss:0.3022\n",
            "timer:  0.4245 sec.\n",
            "-------------\n",
            "Epoch 36/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 36 || Epoch_D_Loss:0.0201 ||Epoch_G_Loss:0.2715 ||Epoch_E_Loss:0.2892\n",
            "timer:  0.4255 sec.\n",
            "-------------\n",
            "Epoch 37/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 37 || Epoch_D_Loss:0.0212 ||Epoch_G_Loss:0.2795 ||Epoch_E_Loss:0.2750\n",
            "timer:  0.4238 sec.\n",
            "-------------\n",
            "Epoch 38/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 38 || Epoch_D_Loss:0.0213 ||Epoch_G_Loss:0.2786 ||Epoch_E_Loss:0.2743\n",
            "timer:  0.4251 sec.\n",
            "-------------\n",
            "Epoch 39/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 39 || Epoch_D_Loss:0.0198 ||Epoch_G_Loss:0.2865 ||Epoch_E_Loss:0.2874\n",
            "timer:  0.4273 sec.\n",
            "-------------\n",
            "Epoch 40/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 40 || Epoch_D_Loss:0.0173 ||Epoch_G_Loss:0.2933 ||Epoch_E_Loss:0.3113\n",
            "timer:  0.4245 sec.\n",
            "-------------\n",
            "Epoch 41/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 41 || Epoch_D_Loss:0.0157 ||Epoch_G_Loss:0.2996 ||Epoch_E_Loss:0.3253\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 42/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 42 || Epoch_D_Loss:0.0151 ||Epoch_G_Loss:0.3057 ||Epoch_E_Loss:0.3295\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 43/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 43 || Epoch_D_Loss:0.0151 ||Epoch_G_Loss:0.3207 ||Epoch_E_Loss:0.3235\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 44/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 44 || Epoch_D_Loss:0.0158 ||Epoch_G_Loss:0.3162 ||Epoch_E_Loss:0.3108\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 45/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 45 || Epoch_D_Loss:0.0155 ||Epoch_G_Loss:0.3257 ||Epoch_E_Loss:0.3159\n",
            "timer:  0.4252 sec.\n",
            "-------------\n",
            "Epoch 46/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 46 || Epoch_D_Loss:0.0138 ||Epoch_G_Loss:0.3370 ||Epoch_E_Loss:0.3310\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 47/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 47 || Epoch_D_Loss:0.0143 ||Epoch_G_Loss:0.3349 ||Epoch_E_Loss:0.3232\n",
            "timer:  0.4249 sec.\n",
            "-------------\n",
            "Epoch 48/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 48 || Epoch_D_Loss:0.0140 ||Epoch_G_Loss:0.3390 ||Epoch_E_Loss:0.3256\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 49/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 49 || Epoch_D_Loss:0.0120 ||Epoch_G_Loss:0.3389 ||Epoch_E_Loss:0.3478\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 50/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 50 || Epoch_D_Loss:0.0113 ||Epoch_G_Loss:0.3431 ||Epoch_E_Loss:0.3523\n",
            "timer:  0.4276 sec.\n",
            "-------------\n",
            "Epoch 51/1500\n",
            "-------------\n",
            "（train）\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "-------------\n",
            "epoch 51 || Epoch_D_Loss:0.0129 ||Epoch_G_Loss:0.3426 ||Epoch_E_Loss:0.3360\n",
            "timer:  0.4296 sec.\n",
            "-------------\n",
            "Epoch 52/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 52 || Epoch_D_Loss:0.0129 ||Epoch_G_Loss:0.3523 ||Epoch_E_Loss:0.3418\n",
            "timer:  0.4292 sec.\n",
            "-------------\n",
            "Epoch 53/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 53 || Epoch_D_Loss:0.0106 ||Epoch_G_Loss:0.3523 ||Epoch_E_Loss:0.3650\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 54/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 54 || Epoch_D_Loss:0.0105 ||Epoch_G_Loss:0.3572 ||Epoch_E_Loss:0.3642\n",
            "timer:  0.4253 sec.\n",
            "-------------\n",
            "Epoch 55/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 55 || Epoch_D_Loss:0.0101 ||Epoch_G_Loss:0.3595 ||Epoch_E_Loss:0.3754\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 56/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 56 || Epoch_D_Loss:0.0087 ||Epoch_G_Loss:0.3768 ||Epoch_E_Loss:0.3977\n",
            "timer:  0.4254 sec.\n",
            "-------------\n",
            "Epoch 57/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 57 || Epoch_D_Loss:0.0076 ||Epoch_G_Loss:0.3829 ||Epoch_E_Loss:0.4093\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 58/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 58 || Epoch_D_Loss:0.0082 ||Epoch_G_Loss:0.3747 ||Epoch_E_Loss:0.3903\n",
            "timer:  0.4251 sec.\n",
            "-------------\n",
            "Epoch 59/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 59 || Epoch_D_Loss:0.0076 ||Epoch_G_Loss:0.3880 ||Epoch_E_Loss:0.4069\n",
            "timer:  0.4252 sec.\n",
            "-------------\n",
            "Epoch 60/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 60 || Epoch_D_Loss:0.0064 ||Epoch_G_Loss:0.3979 ||Epoch_E_Loss:0.4334\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 61/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 61 || Epoch_D_Loss:0.0056 ||Epoch_G_Loss:0.4021 ||Epoch_E_Loss:0.4515\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 62/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 62 || Epoch_D_Loss:0.0057 ||Epoch_G_Loss:0.4080 ||Epoch_E_Loss:0.4484\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 63/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 63 || Epoch_D_Loss:0.0064 ||Epoch_G_Loss:0.4078 ||Epoch_E_Loss:0.4318\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 64/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 64 || Epoch_D_Loss:0.0079 ||Epoch_G_Loss:0.4060 ||Epoch_E_Loss:0.4028\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 65/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 65 || Epoch_D_Loss:0.0086 ||Epoch_G_Loss:0.3931 ||Epoch_E_Loss:0.3984\n",
            "timer:  0.4275 sec.\n",
            "-------------\n",
            "Epoch 66/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 66 || Epoch_D_Loss:0.0075 ||Epoch_G_Loss:0.4049 ||Epoch_E_Loss:0.4162\n",
            "timer:  0.4254 sec.\n",
            "-------------\n",
            "Epoch 67/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 67 || Epoch_D_Loss:0.0063 ||Epoch_G_Loss:0.4189 ||Epoch_E_Loss:0.4384\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 68/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 68 || Epoch_D_Loss:0.0063 ||Epoch_G_Loss:0.4099 ||Epoch_E_Loss:0.4348\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 69/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 69 || Epoch_D_Loss:0.0078 ||Epoch_G_Loss:0.4024 ||Epoch_E_Loss:0.4064\n",
            "timer:  0.4296 sec.\n",
            "-------------\n",
            "Epoch 70/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 70 || Epoch_D_Loss:0.0084 ||Epoch_G_Loss:0.3956 ||Epoch_E_Loss:0.3932\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 71/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 71 || Epoch_D_Loss:0.0079 ||Epoch_G_Loss:0.3997 ||Epoch_E_Loss:0.4040\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 72/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 72 || Epoch_D_Loss:0.0085 ||Epoch_G_Loss:0.4144 ||Epoch_E_Loss:0.4017\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 73/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 73 || Epoch_D_Loss:0.0098 ||Epoch_G_Loss:0.3930 ||Epoch_E_Loss:0.3752\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 74/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 74 || Epoch_D_Loss:0.0134 ||Epoch_G_Loss:0.3717 ||Epoch_E_Loss:0.3687\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 75/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 75 || Epoch_D_Loss:0.0108 ||Epoch_G_Loss:0.3909 ||Epoch_E_Loss:0.3753\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 76/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 76 || Epoch_D_Loss:0.0174 ||Epoch_G_Loss:0.3547 ||Epoch_E_Loss:0.3518\n",
            "timer:  0.4241 sec.\n",
            "-------------\n",
            "Epoch 77/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 77 || Epoch_D_Loss:0.0174 ||Epoch_G_Loss:0.3363 ||Epoch_E_Loss:0.3370\n",
            "timer:  0.4253 sec.\n",
            "-------------\n",
            "Epoch 78/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 78 || Epoch_D_Loss:0.0293 ||Epoch_G_Loss:0.3174 ||Epoch_E_Loss:0.2996\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 79/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 79 || Epoch_D_Loss:0.0158 ||Epoch_G_Loss:0.3419 ||Epoch_E_Loss:0.3305\n",
            "timer:  0.4253 sec.\n",
            "-------------\n",
            "Epoch 80/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 80 || Epoch_D_Loss:0.0132 ||Epoch_G_Loss:0.3569 ||Epoch_E_Loss:0.3417\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 81/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 81 || Epoch_D_Loss:0.0103 ||Epoch_G_Loss:0.3853 ||Epoch_E_Loss:0.3715\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 82/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 82 || Epoch_D_Loss:0.0125 ||Epoch_G_Loss:0.3591 ||Epoch_E_Loss:0.3548\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 83/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 83 || Epoch_D_Loss:0.0177 ||Epoch_G_Loss:0.3583 ||Epoch_E_Loss:0.3183\n",
            "timer:  0.4270 sec.\n",
            "-------------\n",
            "Epoch 84/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 84 || Epoch_D_Loss:0.0187 ||Epoch_G_Loss:0.3333 ||Epoch_E_Loss:0.3611\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 85/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 85 || Epoch_D_Loss:0.0246 ||Epoch_G_Loss:0.3269 ||Epoch_E_Loss:0.3150\n",
            "timer:  0.4250 sec.\n",
            "-------------\n",
            "Epoch 86/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 86 || Epoch_D_Loss:0.0417 ||Epoch_G_Loss:0.3233 ||Epoch_E_Loss:0.3690\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 87/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 87 || Epoch_D_Loss:0.0149 ||Epoch_G_Loss:0.3533 ||Epoch_E_Loss:0.3757\n",
            "timer:  0.4273 sec.\n",
            "-------------\n",
            "Epoch 88/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 88 || Epoch_D_Loss:0.0187 ||Epoch_G_Loss:0.3338 ||Epoch_E_Loss:0.3422\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 89/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 89 || Epoch_D_Loss:0.0323 ||Epoch_G_Loss:0.2847 ||Epoch_E_Loss:0.2963\n",
            "timer:  0.4280 sec.\n",
            "-------------\n",
            "Epoch 90/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 90 || Epoch_D_Loss:0.0399 ||Epoch_G_Loss:0.2798 ||Epoch_E_Loss:0.2911\n",
            "timer:  0.4270 sec.\n",
            "-------------\n",
            "Epoch 91/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 91 || Epoch_D_Loss:0.0338 ||Epoch_G_Loss:0.2968 ||Epoch_E_Loss:0.3038\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 92/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 92 || Epoch_D_Loss:0.0357 ||Epoch_G_Loss:0.2756 ||Epoch_E_Loss:0.2813\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 93/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 93 || Epoch_D_Loss:0.0310 ||Epoch_G_Loss:0.2768 ||Epoch_E_Loss:0.3085\n",
            "timer:  0.4297 sec.\n",
            "-------------\n",
            "Epoch 94/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 94 || Epoch_D_Loss:0.0327 ||Epoch_G_Loss:0.3151 ||Epoch_E_Loss:0.2926\n",
            "timer:  0.4286 sec.\n",
            "-------------\n",
            "Epoch 95/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 95 || Epoch_D_Loss:0.0344 ||Epoch_G_Loss:0.2965 ||Epoch_E_Loss:0.3171\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 96/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 96 || Epoch_D_Loss:0.0293 ||Epoch_G_Loss:0.3107 ||Epoch_E_Loss:0.3197\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 97/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 97 || Epoch_D_Loss:0.0333 ||Epoch_G_Loss:0.2951 ||Epoch_E_Loss:0.3067\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 98/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 98 || Epoch_D_Loss:0.0428 ||Epoch_G_Loss:0.2539 ||Epoch_E_Loss:0.2886\n",
            "timer:  0.4253 sec.\n",
            "-------------\n",
            "Epoch 99/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 99 || Epoch_D_Loss:0.0700 ||Epoch_G_Loss:0.2341 ||Epoch_E_Loss:0.2685\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 100/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 100 || Epoch_D_Loss:0.0483 ||Epoch_G_Loss:0.2721 ||Epoch_E_Loss:0.2704\n",
            "timer:  0.4289 sec.\n",
            "-------------\n",
            "Epoch 101/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 101 || Epoch_D_Loss:0.0426 ||Epoch_G_Loss:0.2782 ||Epoch_E_Loss:0.2938\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 102/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 102 || Epoch_D_Loss:0.0349 ||Epoch_G_Loss:0.3181 ||Epoch_E_Loss:0.2992\n",
            "timer:  0.4282 sec.\n",
            "-------------\n",
            "Epoch 103/1500\n",
            "-------------\n",
            "（train）\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "-------------\n",
            "epoch 103 || Epoch_D_Loss:0.0389 ||Epoch_G_Loss:0.2903 ||Epoch_E_Loss:0.3237\n",
            "timer:  0.4284 sec.\n",
            "-------------\n",
            "Epoch 104/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 104 || Epoch_D_Loss:0.0520 ||Epoch_G_Loss:0.2650 ||Epoch_E_Loss:0.2816\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 105/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 105 || Epoch_D_Loss:0.0804 ||Epoch_G_Loss:0.2246 ||Epoch_E_Loss:0.2261\n",
            "timer:  0.4276 sec.\n",
            "-------------\n",
            "Epoch 106/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 106 || Epoch_D_Loss:0.0669 ||Epoch_G_Loss:0.2189 ||Epoch_E_Loss:0.2451\n",
            "timer:  0.4252 sec.\n",
            "-------------\n",
            "Epoch 107/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 107 || Epoch_D_Loss:0.0579 ||Epoch_G_Loss:0.2306 ||Epoch_E_Loss:0.2647\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 108/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 108 || Epoch_D_Loss:0.0629 ||Epoch_G_Loss:0.2690 ||Epoch_E_Loss:0.2621\n",
            "timer:  0.4270 sec.\n",
            "-------------\n",
            "Epoch 109/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 109 || Epoch_D_Loss:0.0574 ||Epoch_G_Loss:0.2387 ||Epoch_E_Loss:0.2449\n",
            "timer:  0.4254 sec.\n",
            "-------------\n",
            "Epoch 110/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 110 || Epoch_D_Loss:0.1087 ||Epoch_G_Loss:0.1699 ||Epoch_E_Loss:0.1919\n",
            "timer:  0.4248 sec.\n",
            "-------------\n",
            "Epoch 111/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 111 || Epoch_D_Loss:0.0862 ||Epoch_G_Loss:0.1889 ||Epoch_E_Loss:0.2240\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 112/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 112 || Epoch_D_Loss:0.0471 ||Epoch_G_Loss:0.2467 ||Epoch_E_Loss:0.2517\n",
            "timer:  0.4253 sec.\n",
            "-------------\n",
            "Epoch 113/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 113 || Epoch_D_Loss:0.0552 ||Epoch_G_Loss:0.2061 ||Epoch_E_Loss:0.2422\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 114/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 114 || Epoch_D_Loss:0.0751 ||Epoch_G_Loss:0.1905 ||Epoch_E_Loss:0.2007\n",
            "timer:  0.4281 sec.\n",
            "-------------\n",
            "Epoch 115/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 115 || Epoch_D_Loss:0.0853 ||Epoch_G_Loss:0.1685 ||Epoch_E_Loss:0.2221\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 116/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 116 || Epoch_D_Loss:0.0751 ||Epoch_G_Loss:0.2002 ||Epoch_E_Loss:0.2102\n",
            "timer:  0.4279 sec.\n",
            "-------------\n",
            "Epoch 117/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 117 || Epoch_D_Loss:0.0561 ||Epoch_G_Loss:0.2302 ||Epoch_E_Loss:0.2322\n",
            "timer:  0.4296 sec.\n",
            "-------------\n",
            "Epoch 118/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 118 || Epoch_D_Loss:0.0450 ||Epoch_G_Loss:0.2493 ||Epoch_E_Loss:0.2528\n",
            "timer:  0.4258 sec.\n",
            "-------------\n",
            "Epoch 119/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 119 || Epoch_D_Loss:0.0497 ||Epoch_G_Loss:0.2231 ||Epoch_E_Loss:0.2401\n",
            "timer:  0.4250 sec.\n",
            "-------------\n",
            "Epoch 120/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 120 || Epoch_D_Loss:0.0522 ||Epoch_G_Loss:0.2130 ||Epoch_E_Loss:0.2389\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 121/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 121 || Epoch_D_Loss:0.0487 ||Epoch_G_Loss:0.2294 ||Epoch_E_Loss:0.2472\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 122/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 122 || Epoch_D_Loss:0.0521 ||Epoch_G_Loss:0.2280 ||Epoch_E_Loss:0.2589\n",
            "timer:  0.4253 sec.\n",
            "-------------\n",
            "Epoch 123/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 123 || Epoch_D_Loss:0.0643 ||Epoch_G_Loss:0.2279 ||Epoch_E_Loss:0.2208\n",
            "timer:  0.4255 sec.\n",
            "-------------\n",
            "Epoch 124/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 124 || Epoch_D_Loss:0.0777 ||Epoch_G_Loss:0.1852 ||Epoch_E_Loss:0.2439\n",
            "timer:  0.4277 sec.\n",
            "-------------\n",
            "Epoch 125/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 125 || Epoch_D_Loss:0.0753 ||Epoch_G_Loss:0.2065 ||Epoch_E_Loss:0.2231\n",
            "timer:  0.4270 sec.\n",
            "-------------\n",
            "Epoch 126/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 126 || Epoch_D_Loss:0.0632 ||Epoch_G_Loss:0.2121 ||Epoch_E_Loss:0.2512\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 127/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 127 || Epoch_D_Loss:0.0442 ||Epoch_G_Loss:0.2444 ||Epoch_E_Loss:0.2863\n",
            "timer:  0.4277 sec.\n",
            "-------------\n",
            "Epoch 128/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 128 || Epoch_D_Loss:0.0467 ||Epoch_G_Loss:0.2304 ||Epoch_E_Loss:0.2803\n",
            "timer:  0.4249 sec.\n",
            "-------------\n",
            "Epoch 129/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 129 || Epoch_D_Loss:0.0733 ||Epoch_G_Loss:0.1971 ||Epoch_E_Loss:0.2330\n",
            "timer:  0.4249 sec.\n",
            "-------------\n",
            "Epoch 130/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 130 || Epoch_D_Loss:0.0946 ||Epoch_G_Loss:0.1652 ||Epoch_E_Loss:0.1954\n",
            "timer:  0.4252 sec.\n",
            "-------------\n",
            "Epoch 131/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 131 || Epoch_D_Loss:0.0970 ||Epoch_G_Loss:0.1718 ||Epoch_E_Loss:0.1877\n",
            "timer:  0.4253 sec.\n",
            "-------------\n",
            "Epoch 132/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 132 || Epoch_D_Loss:0.0948 ||Epoch_G_Loss:0.1808 ||Epoch_E_Loss:0.1941\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 133/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 133 || Epoch_D_Loss:0.1078 ||Epoch_G_Loss:0.1676 ||Epoch_E_Loss:0.2025\n",
            "timer:  0.4276 sec.\n",
            "-------------\n",
            "Epoch 134/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 134 || Epoch_D_Loss:0.0805 ||Epoch_G_Loss:0.1785 ||Epoch_E_Loss:0.2077\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 135/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 135 || Epoch_D_Loss:0.0653 ||Epoch_G_Loss:0.2130 ||Epoch_E_Loss:0.2118\n",
            "timer:  0.4254 sec.\n",
            "-------------\n",
            "Epoch 136/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 136 || Epoch_D_Loss:0.0654 ||Epoch_G_Loss:0.2127 ||Epoch_E_Loss:0.2152\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 137/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 137 || Epoch_D_Loss:0.0852 ||Epoch_G_Loss:0.1796 ||Epoch_E_Loss:0.1822\n",
            "timer:  0.4271 sec.\n",
            "-------------\n",
            "Epoch 138/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 138 || Epoch_D_Loss:0.0876 ||Epoch_G_Loss:0.1610 ||Epoch_E_Loss:0.1904\n",
            "timer:  0.4478 sec.\n",
            "-------------\n",
            "Epoch 139/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 139 || Epoch_D_Loss:0.0831 ||Epoch_G_Loss:0.1726 ||Epoch_E_Loss:0.2043\n",
            "timer:  0.4631 sec.\n",
            "-------------\n",
            "Epoch 140/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 140 || Epoch_D_Loss:0.0861 ||Epoch_G_Loss:0.1815 ||Epoch_E_Loss:0.1862\n",
            "timer:  0.4482 sec.\n",
            "-------------\n",
            "Epoch 141/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 141 || Epoch_D_Loss:0.0869 ||Epoch_G_Loss:0.1586 ||Epoch_E_Loss:0.1869\n",
            "timer:  0.4283 sec.\n",
            "-------------\n",
            "Epoch 142/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 142 || Epoch_D_Loss:0.0831 ||Epoch_G_Loss:0.1836 ||Epoch_E_Loss:0.1732\n",
            "timer:  0.4355 sec.\n",
            "-------------\n",
            "Epoch 143/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 143 || Epoch_D_Loss:0.0805 ||Epoch_G_Loss:0.1807 ||Epoch_E_Loss:0.1808\n",
            "timer:  0.4424 sec.\n",
            "-------------\n",
            "Epoch 144/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 144 || Epoch_D_Loss:0.0859 ||Epoch_G_Loss:0.1656 ||Epoch_E_Loss:0.1729\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 145/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 145 || Epoch_D_Loss:0.1045 ||Epoch_G_Loss:0.1330 ||Epoch_E_Loss:0.1575\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 146/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 146 || Epoch_D_Loss:0.1243 ||Epoch_G_Loss:0.1226 ||Epoch_E_Loss:0.1469\n",
            "timer:  0.4238 sec.\n",
            "-------------\n",
            "Epoch 147/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 147 || Epoch_D_Loss:0.0980 ||Epoch_G_Loss:0.1523 ||Epoch_E_Loss:0.1735\n",
            "timer:  0.4236 sec.\n",
            "-------------\n",
            "Epoch 148/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 148 || Epoch_D_Loss:0.0833 ||Epoch_G_Loss:0.1543 ||Epoch_E_Loss:0.1891\n",
            "timer:  0.4244 sec.\n",
            "-------------\n",
            "Epoch 149/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 149 || Epoch_D_Loss:0.0895 ||Epoch_G_Loss:0.1756 ||Epoch_E_Loss:0.1727\n",
            "timer:  0.4251 sec.\n",
            "-------------\n",
            "Epoch 150/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 150 || Epoch_D_Loss:0.0844 ||Epoch_G_Loss:0.1683 ||Epoch_E_Loss:0.1772\n",
            "timer:  0.4253 sec.\n",
            "-------------\n",
            "Epoch 151/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 151 || Epoch_D_Loss:0.0837 ||Epoch_G_Loss:0.1888 ||Epoch_E_Loss:0.1697\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 152/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 152 || Epoch_D_Loss:0.0768 ||Epoch_G_Loss:0.1814 ||Epoch_E_Loss:0.1808\n",
            "timer:  0.4253 sec.\n",
            "-------------\n",
            "Epoch 153/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 153 || Epoch_D_Loss:0.0864 ||Epoch_G_Loss:0.1752 ||Epoch_E_Loss:0.1739\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 154/1500\n",
            "-------------\n",
            "（train）\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "-------------\n",
            "epoch 154 || Epoch_D_Loss:0.0929 ||Epoch_G_Loss:0.1596 ||Epoch_E_Loss:0.1787\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 155/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 155 || Epoch_D_Loss:0.1010 ||Epoch_G_Loss:0.1715 ||Epoch_E_Loss:0.1699\n",
            "timer:  0.4248 sec.\n",
            "-------------\n",
            "Epoch 156/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 156 || Epoch_D_Loss:0.1025 ||Epoch_G_Loss:0.1491 ||Epoch_E_Loss:0.1676\n",
            "timer:  0.4244 sec.\n",
            "-------------\n",
            "Epoch 157/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 157 || Epoch_D_Loss:0.1098 ||Epoch_G_Loss:0.1548 ||Epoch_E_Loss:0.1706\n",
            "timer:  0.4247 sec.\n",
            "-------------\n",
            "Epoch 158/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 158 || Epoch_D_Loss:0.1070 ||Epoch_G_Loss:0.1583 ||Epoch_E_Loss:0.1645\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 159/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 159 || Epoch_D_Loss:0.0915 ||Epoch_G_Loss:0.1663 ||Epoch_E_Loss:0.1783\n",
            "timer:  0.4258 sec.\n",
            "-------------\n",
            "Epoch 160/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 160 || Epoch_D_Loss:0.0978 ||Epoch_G_Loss:0.1666 ||Epoch_E_Loss:0.1689\n",
            "timer:  0.4276 sec.\n",
            "-------------\n",
            "Epoch 161/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 161 || Epoch_D_Loss:0.1017 ||Epoch_G_Loss:0.1644 ||Epoch_E_Loss:0.1554\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 162/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 162 || Epoch_D_Loss:0.1121 ||Epoch_G_Loss:0.1394 ||Epoch_E_Loss:0.1561\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 163/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 163 || Epoch_D_Loss:0.1160 ||Epoch_G_Loss:0.1356 ||Epoch_E_Loss:0.1495\n",
            "timer:  0.4278 sec.\n",
            "-------------\n",
            "Epoch 164/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 164 || Epoch_D_Loss:0.1067 ||Epoch_G_Loss:0.1524 ||Epoch_E_Loss:0.1560\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 165/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 165 || Epoch_D_Loss:0.0965 ||Epoch_G_Loss:0.1485 ||Epoch_E_Loss:0.1617\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 166/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 166 || Epoch_D_Loss:0.0913 ||Epoch_G_Loss:0.1498 ||Epoch_E_Loss:0.1772\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 167/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 167 || Epoch_D_Loss:0.0823 ||Epoch_G_Loss:0.1684 ||Epoch_E_Loss:0.1796\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 168/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 168 || Epoch_D_Loss:0.0827 ||Epoch_G_Loss:0.1759 ||Epoch_E_Loss:0.1763\n",
            "timer:  0.4254 sec.\n",
            "-------------\n",
            "Epoch 169/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 169 || Epoch_D_Loss:0.0906 ||Epoch_G_Loss:0.1706 ||Epoch_E_Loss:0.1746\n",
            "timer:  0.4271 sec.\n",
            "-------------\n",
            "Epoch 170/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 170 || Epoch_D_Loss:0.1006 ||Epoch_G_Loss:0.1538 ||Epoch_E_Loss:0.1626\n",
            "timer:  0.4283 sec.\n",
            "-------------\n",
            "Epoch 171/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 171 || Epoch_D_Loss:0.1197 ||Epoch_G_Loss:0.1366 ||Epoch_E_Loss:0.1545\n",
            "timer:  0.4280 sec.\n",
            "-------------\n",
            "Epoch 172/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 172 || Epoch_D_Loss:0.1343 ||Epoch_G_Loss:0.1280 ||Epoch_E_Loss:0.1368\n",
            "timer:  0.4277 sec.\n",
            "-------------\n",
            "Epoch 173/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 173 || Epoch_D_Loss:0.1244 ||Epoch_G_Loss:0.1315 ||Epoch_E_Loss:0.1438\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 174/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 174 || Epoch_D_Loss:0.1136 ||Epoch_G_Loss:0.1458 ||Epoch_E_Loss:0.1509\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 175/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 175 || Epoch_D_Loss:0.1016 ||Epoch_G_Loss:0.1429 ||Epoch_E_Loss:0.1571\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 176/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 176 || Epoch_D_Loss:0.0992 ||Epoch_G_Loss:0.1592 ||Epoch_E_Loss:0.1583\n",
            "timer:  0.4271 sec.\n",
            "-------------\n",
            "Epoch 177/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 177 || Epoch_D_Loss:0.0914 ||Epoch_G_Loss:0.1581 ||Epoch_E_Loss:0.1592\n",
            "timer:  0.4275 sec.\n",
            "-------------\n",
            "Epoch 178/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 178 || Epoch_D_Loss:0.0960 ||Epoch_G_Loss:0.1518 ||Epoch_E_Loss:0.1546\n",
            "timer:  0.4285 sec.\n",
            "-------------\n",
            "Epoch 179/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 179 || Epoch_D_Loss:0.1039 ||Epoch_G_Loss:0.1458 ||Epoch_E_Loss:0.1463\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 180/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 180 || Epoch_D_Loss:0.1099 ||Epoch_G_Loss:0.1453 ||Epoch_E_Loss:0.1392\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 181/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 181 || Epoch_D_Loss:0.1274 ||Epoch_G_Loss:0.1410 ||Epoch_E_Loss:0.1322\n",
            "timer:  0.4251 sec.\n",
            "-------------\n",
            "Epoch 182/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 182 || Epoch_D_Loss:0.1306 ||Epoch_G_Loss:0.1164 ||Epoch_E_Loss:0.1296\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 183/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 183 || Epoch_D_Loss:0.1275 ||Epoch_G_Loss:0.1342 ||Epoch_E_Loss:0.1281\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 184/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 184 || Epoch_D_Loss:0.1159 ||Epoch_G_Loss:0.1388 ||Epoch_E_Loss:0.1358\n",
            "timer:  0.4270 sec.\n",
            "-------------\n",
            "Epoch 185/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 185 || Epoch_D_Loss:0.1018 ||Epoch_G_Loss:0.1527 ||Epoch_E_Loss:0.1514\n",
            "timer:  0.4258 sec.\n",
            "-------------\n",
            "Epoch 186/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 186 || Epoch_D_Loss:0.0947 ||Epoch_G_Loss:0.1417 ||Epoch_E_Loss:0.1557\n",
            "timer:  0.4271 sec.\n",
            "-------------\n",
            "Epoch 187/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 187 || Epoch_D_Loss:0.1009 ||Epoch_G_Loss:0.1677 ||Epoch_E_Loss:0.1512\n",
            "timer:  0.4298 sec.\n",
            "-------------\n",
            "Epoch 188/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 188 || Epoch_D_Loss:0.0983 ||Epoch_G_Loss:0.1409 ||Epoch_E_Loss:0.1544\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 189/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 189 || Epoch_D_Loss:0.1104 ||Epoch_G_Loss:0.1538 ||Epoch_E_Loss:0.1411\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 190/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 190 || Epoch_D_Loss:0.1188 ||Epoch_G_Loss:0.1446 ||Epoch_E_Loss:0.1415\n",
            "timer:  0.4258 sec.\n",
            "-------------\n",
            "Epoch 191/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 191 || Epoch_D_Loss:0.1401 ||Epoch_G_Loss:0.1306 ||Epoch_E_Loss:0.1295\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 192/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 192 || Epoch_D_Loss:0.1317 ||Epoch_G_Loss:0.1431 ||Epoch_E_Loss:0.1347\n",
            "timer:  0.4278 sec.\n",
            "-------------\n",
            "Epoch 193/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 193 || Epoch_D_Loss:0.1170 ||Epoch_G_Loss:0.1432 ||Epoch_E_Loss:0.1397\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 194/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 194 || Epoch_D_Loss:0.0986 ||Epoch_G_Loss:0.1509 ||Epoch_E_Loss:0.1570\n",
            "timer:  0.4279 sec.\n",
            "-------------\n",
            "Epoch 195/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 195 || Epoch_D_Loss:0.0940 ||Epoch_G_Loss:0.1583 ||Epoch_E_Loss:0.1686\n",
            "timer:  0.4296 sec.\n",
            "-------------\n",
            "Epoch 196/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 196 || Epoch_D_Loss:0.0895 ||Epoch_G_Loss:0.1779 ||Epoch_E_Loss:0.1691\n",
            "timer:  0.4271 sec.\n",
            "-------------\n",
            "Epoch 197/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 197 || Epoch_D_Loss:0.0938 ||Epoch_G_Loss:0.1577 ||Epoch_E_Loss:0.1684\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 198/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 198 || Epoch_D_Loss:0.1101 ||Epoch_G_Loss:0.1556 ||Epoch_E_Loss:0.1516\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 199/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 199 || Epoch_D_Loss:0.1181 ||Epoch_G_Loss:0.1479 ||Epoch_E_Loss:0.1462\n",
            "timer:  0.4291 sec.\n",
            "-------------\n",
            "Epoch 200/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 200 || Epoch_D_Loss:0.1338 ||Epoch_G_Loss:0.1352 ||Epoch_E_Loss:0.1277\n",
            "timer:  0.4277 sec.\n",
            "-------------\n",
            "Epoch 201/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 201 || Epoch_D_Loss:0.1387 ||Epoch_G_Loss:0.1245 ||Epoch_E_Loss:0.1280\n",
            "timer:  0.4255 sec.\n",
            "-------------\n",
            "Epoch 202/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 202 || Epoch_D_Loss:0.1344 ||Epoch_G_Loss:0.1131 ||Epoch_E_Loss:0.1275\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 203/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 203 || Epoch_D_Loss:0.1329 ||Epoch_G_Loss:0.1212 ||Epoch_E_Loss:0.1290\n",
            "timer:  0.4255 sec.\n",
            "-------------\n",
            "Epoch 204/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 204 || Epoch_D_Loss:0.1160 ||Epoch_G_Loss:0.1286 ||Epoch_E_Loss:0.1381\n",
            "timer:  0.4271 sec.\n",
            "-------------\n",
            "Epoch 205/1500\n",
            "-------------\n",
            "（train）\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "-------------\n",
            "epoch 205 || Epoch_D_Loss:0.1144 ||Epoch_G_Loss:0.1385 ||Epoch_E_Loss:0.1391\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 206/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 206 || Epoch_D_Loss:0.1105 ||Epoch_G_Loss:0.1335 ||Epoch_E_Loss:0.1395\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 207/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 207 || Epoch_D_Loss:0.1172 ||Epoch_G_Loss:0.1623 ||Epoch_E_Loss:0.1339\n",
            "timer:  0.4254 sec.\n",
            "-------------\n",
            "Epoch 208/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 208 || Epoch_D_Loss:0.1146 ||Epoch_G_Loss:0.1114 ||Epoch_E_Loss:0.1431\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 209/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 209 || Epoch_D_Loss:0.1484 ||Epoch_G_Loss:0.1176 ||Epoch_E_Loss:0.1145\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 210/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 210 || Epoch_D_Loss:0.1496 ||Epoch_G_Loss:0.1111 ||Epoch_E_Loss:0.1165\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 211/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 211 || Epoch_D_Loss:0.1451 ||Epoch_G_Loss:0.1136 ||Epoch_E_Loss:0.1165\n",
            "timer:  0.4254 sec.\n",
            "-------------\n",
            "Epoch 212/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 212 || Epoch_D_Loss:0.1549 ||Epoch_G_Loss:0.1025 ||Epoch_E_Loss:0.1129\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 213/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 213 || Epoch_D_Loss:0.1440 ||Epoch_G_Loss:0.1033 ||Epoch_E_Loss:0.1209\n",
            "timer:  0.4253 sec.\n",
            "-------------\n",
            "Epoch 214/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 214 || Epoch_D_Loss:0.1336 ||Epoch_G_Loss:0.1242 ||Epoch_E_Loss:0.1211\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 215/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 215 || Epoch_D_Loss:0.1258 ||Epoch_G_Loss:0.1224 ||Epoch_E_Loss:0.1270\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 216/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 216 || Epoch_D_Loss:0.1178 ||Epoch_G_Loss:0.1248 ||Epoch_E_Loss:0.1324\n",
            "timer:  0.4253 sec.\n",
            "-------------\n",
            "Epoch 217/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 217 || Epoch_D_Loss:0.1181 ||Epoch_G_Loss:0.1215 ||Epoch_E_Loss:0.1294\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 218/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 218 || Epoch_D_Loss:0.1220 ||Epoch_G_Loss:0.1226 ||Epoch_E_Loss:0.1276\n",
            "timer:  0.4290 sec.\n",
            "-------------\n",
            "Epoch 219/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 219 || Epoch_D_Loss:0.1132 ||Epoch_G_Loss:0.1357 ||Epoch_E_Loss:0.1331\n",
            "timer:  0.4249 sec.\n",
            "-------------\n",
            "Epoch 220/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 220 || Epoch_D_Loss:0.1262 ||Epoch_G_Loss:0.1204 ||Epoch_E_Loss:0.1218\n",
            "timer:  0.4253 sec.\n",
            "-------------\n",
            "Epoch 221/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 221 || Epoch_D_Loss:0.1296 ||Epoch_G_Loss:0.1289 ||Epoch_E_Loss:0.1302\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 222/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 222 || Epoch_D_Loss:0.1196 ||Epoch_G_Loss:0.1274 ||Epoch_E_Loss:0.1298\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 223/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 223 || Epoch_D_Loss:0.1139 ||Epoch_G_Loss:0.1377 ||Epoch_E_Loss:0.1354\n",
            "timer:  0.4251 sec.\n",
            "-------------\n",
            "Epoch 224/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 224 || Epoch_D_Loss:0.1182 ||Epoch_G_Loss:0.1306 ||Epoch_E_Loss:0.1312\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 225/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 225 || Epoch_D_Loss:0.1256 ||Epoch_G_Loss:0.1185 ||Epoch_E_Loss:0.1278\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 226/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 226 || Epoch_D_Loss:0.1309 ||Epoch_G_Loss:0.1075 ||Epoch_E_Loss:0.1279\n",
            "timer:  0.4249 sec.\n",
            "-------------\n",
            "Epoch 227/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 227 || Epoch_D_Loss:0.1229 ||Epoch_G_Loss:0.1282 ||Epoch_E_Loss:0.1305\n",
            "timer:  0.4250 sec.\n",
            "-------------\n",
            "Epoch 228/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 228 || Epoch_D_Loss:0.1149 ||Epoch_G_Loss:0.1220 ||Epoch_E_Loss:0.1359\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 229/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 229 || Epoch_D_Loss:0.1167 ||Epoch_G_Loss:0.1323 ||Epoch_E_Loss:0.1340\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 230/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 230 || Epoch_D_Loss:0.1253 ||Epoch_G_Loss:0.1190 ||Epoch_E_Loss:0.1385\n",
            "timer:  0.4279 sec.\n",
            "-------------\n",
            "Epoch 231/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 231 || Epoch_D_Loss:0.1249 ||Epoch_G_Loss:0.1218 ||Epoch_E_Loss:0.1335\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 232/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 232 || Epoch_D_Loss:0.1197 ||Epoch_G_Loss:0.1279 ||Epoch_E_Loss:0.1375\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 233/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 233 || Epoch_D_Loss:0.1285 ||Epoch_G_Loss:0.1313 ||Epoch_E_Loss:0.1259\n",
            "timer:  0.4278 sec.\n",
            "-------------\n",
            "Epoch 234/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 234 || Epoch_D_Loss:0.1193 ||Epoch_G_Loss:0.1305 ||Epoch_E_Loss:0.1357\n",
            "timer:  0.4273 sec.\n",
            "-------------\n",
            "Epoch 235/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 235 || Epoch_D_Loss:0.1163 ||Epoch_G_Loss:0.1279 ||Epoch_E_Loss:0.1372\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 236/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 236 || Epoch_D_Loss:0.1181 ||Epoch_G_Loss:0.1115 ||Epoch_E_Loss:0.1377\n",
            "timer:  0.4278 sec.\n",
            "-------------\n",
            "Epoch 237/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 237 || Epoch_D_Loss:0.1217 ||Epoch_G_Loss:0.1421 ||Epoch_E_Loss:0.1284\n",
            "timer:  0.4258 sec.\n",
            "-------------\n",
            "Epoch 238/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 238 || Epoch_D_Loss:0.1149 ||Epoch_G_Loss:0.1390 ||Epoch_E_Loss:0.1403\n",
            "timer:  0.4279 sec.\n",
            "-------------\n",
            "Epoch 239/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 239 || Epoch_D_Loss:0.1061 ||Epoch_G_Loss:0.1367 ||Epoch_E_Loss:0.1473\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 240/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 240 || Epoch_D_Loss:0.1177 ||Epoch_G_Loss:0.1402 ||Epoch_E_Loss:0.1364\n",
            "timer:  0.4253 sec.\n",
            "-------------\n",
            "Epoch 241/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 241 || Epoch_D_Loss:0.1362 ||Epoch_G_Loss:0.1359 ||Epoch_E_Loss:0.1233\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 242/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 242 || Epoch_D_Loss:0.1373 ||Epoch_G_Loss:0.1205 ||Epoch_E_Loss:0.1318\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 243/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 243 || Epoch_D_Loss:0.1361 ||Epoch_G_Loss:0.1190 ||Epoch_E_Loss:0.1280\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 244/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 244 || Epoch_D_Loss:0.1305 ||Epoch_G_Loss:0.1294 ||Epoch_E_Loss:0.1336\n",
            "timer:  0.4255 sec.\n",
            "-------------\n",
            "Epoch 245/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 245 || Epoch_D_Loss:0.1125 ||Epoch_G_Loss:0.1501 ||Epoch_E_Loss:0.1467\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 246/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 246 || Epoch_D_Loss:0.1218 ||Epoch_G_Loss:0.1356 ||Epoch_E_Loss:0.1396\n",
            "timer:  0.4271 sec.\n",
            "-------------\n",
            "Epoch 247/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 247 || Epoch_D_Loss:0.1303 ||Epoch_G_Loss:0.1352 ||Epoch_E_Loss:0.1350\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 248/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 248 || Epoch_D_Loss:0.1348 ||Epoch_G_Loss:0.1252 ||Epoch_E_Loss:0.1346\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 249/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 249 || Epoch_D_Loss:0.1260 ||Epoch_G_Loss:0.1354 ||Epoch_E_Loss:0.1394\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 250/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 250 || Epoch_D_Loss:0.1177 ||Epoch_G_Loss:0.1459 ||Epoch_E_Loss:0.1455\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 251/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 251 || Epoch_D_Loss:0.1249 ||Epoch_G_Loss:0.1395 ||Epoch_E_Loss:0.1422\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 252/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 252 || Epoch_D_Loss:0.1391 ||Epoch_G_Loss:0.1272 ||Epoch_E_Loss:0.1350\n",
            "timer:  0.4273 sec.\n",
            "-------------\n",
            "Epoch 253/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 253 || Epoch_D_Loss:0.1427 ||Epoch_G_Loss:0.1173 ||Epoch_E_Loss:0.1298\n",
            "timer:  0.4280 sec.\n",
            "-------------\n",
            "Epoch 254/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 254 || Epoch_D_Loss:0.1511 ||Epoch_G_Loss:0.1115 ||Epoch_E_Loss:0.1267\n",
            "timer:  0.4254 sec.\n",
            "-------------\n",
            "Epoch 255/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 255 || Epoch_D_Loss:0.1414 ||Epoch_G_Loss:0.1200 ||Epoch_E_Loss:0.1320\n",
            "timer:  0.4279 sec.\n",
            "-------------\n",
            "Epoch 256/1500\n",
            "-------------\n",
            "（train）\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "-------------\n",
            "epoch 256 || Epoch_D_Loss:0.1426 ||Epoch_G_Loss:0.1168 ||Epoch_E_Loss:0.1388\n",
            "timer:  0.4276 sec.\n",
            "-------------\n",
            "Epoch 257/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 257 || Epoch_D_Loss:0.1614 ||Epoch_G_Loss:0.1039 ||Epoch_E_Loss:0.1286\n",
            "timer:  0.4248 sec.\n",
            "-------------\n",
            "Epoch 258/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 258 || Epoch_D_Loss:0.1793 ||Epoch_G_Loss:0.0987 ||Epoch_E_Loss:0.1131\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 259/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 259 || Epoch_D_Loss:0.1798 ||Epoch_G_Loss:0.0942 ||Epoch_E_Loss:0.1113\n",
            "timer:  0.4251 sec.\n",
            "-------------\n",
            "Epoch 260/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 260 || Epoch_D_Loss:0.1724 ||Epoch_G_Loss:0.1033 ||Epoch_E_Loss:0.1091\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 261/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 261 || Epoch_D_Loss:0.1690 ||Epoch_G_Loss:0.0986 ||Epoch_E_Loss:0.1048\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 262/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 262 || Epoch_D_Loss:0.1796 ||Epoch_G_Loss:0.0908 ||Epoch_E_Loss:0.1072\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 263/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 263 || Epoch_D_Loss:0.1885 ||Epoch_G_Loss:0.0845 ||Epoch_E_Loss:0.0927\n",
            "timer:  0.4243 sec.\n",
            "-------------\n",
            "Epoch 264/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 264 || Epoch_D_Loss:0.1872 ||Epoch_G_Loss:0.0811 ||Epoch_E_Loss:0.1001\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 265/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 265 || Epoch_D_Loss:0.1575 ||Epoch_G_Loss:0.0874 ||Epoch_E_Loss:0.1076\n",
            "timer:  0.4283 sec.\n",
            "-------------\n",
            "Epoch 266/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 266 || Epoch_D_Loss:0.1262 ||Epoch_G_Loss:0.1158 ||Epoch_E_Loss:0.1302\n",
            "timer:  0.4282 sec.\n",
            "-------------\n",
            "Epoch 267/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 267 || Epoch_D_Loss:0.1063 ||Epoch_G_Loss:0.1464 ||Epoch_E_Loss:0.1401\n",
            "timer:  0.4278 sec.\n",
            "-------------\n",
            "Epoch 268/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 268 || Epoch_D_Loss:0.1109 ||Epoch_G_Loss:0.1275 ||Epoch_E_Loss:0.1405\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 269/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 269 || Epoch_D_Loss:0.1419 ||Epoch_G_Loss:0.1070 ||Epoch_E_Loss:0.1184\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 270/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 270 || Epoch_D_Loss:0.1486 ||Epoch_G_Loss:0.1027 ||Epoch_E_Loss:0.1164\n",
            "timer:  0.4248 sec.\n",
            "-------------\n",
            "Epoch 271/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 271 || Epoch_D_Loss:0.1351 ||Epoch_G_Loss:0.1026 ||Epoch_E_Loss:0.1249\n",
            "timer:  0.4252 sec.\n",
            "-------------\n",
            "Epoch 272/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 272 || Epoch_D_Loss:0.1578 ||Epoch_G_Loss:0.0896 ||Epoch_E_Loss:0.1055\n",
            "timer:  0.4255 sec.\n",
            "-------------\n",
            "Epoch 273/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 273 || Epoch_D_Loss:0.1738 ||Epoch_G_Loss:0.0882 ||Epoch_E_Loss:0.0974\n",
            "timer:  0.4246 sec.\n",
            "-------------\n",
            "Epoch 274/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 274 || Epoch_D_Loss:0.1344 ||Epoch_G_Loss:0.1100 ||Epoch_E_Loss:0.1201\n",
            "timer:  0.4258 sec.\n",
            "-------------\n",
            "Epoch 275/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 275 || Epoch_D_Loss:0.1229 ||Epoch_G_Loss:0.1172 ||Epoch_E_Loss:0.1285\n",
            "timer:  0.4277 sec.\n",
            "-------------\n",
            "Epoch 276/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 276 || Epoch_D_Loss:0.1384 ||Epoch_G_Loss:0.1090 ||Epoch_E_Loss:0.1209\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 277/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 277 || Epoch_D_Loss:0.1213 ||Epoch_G_Loss:0.1143 ||Epoch_E_Loss:0.1317\n",
            "timer:  0.4279 sec.\n",
            "-------------\n",
            "Epoch 278/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 278 || Epoch_D_Loss:0.1322 ||Epoch_G_Loss:0.1213 ||Epoch_E_Loss:0.1301\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 279/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 279 || Epoch_D_Loss:0.1607 ||Epoch_G_Loss:0.0849 ||Epoch_E_Loss:0.1078\n",
            "timer:  0.4284 sec.\n",
            "-------------\n",
            "Epoch 280/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 280 || Epoch_D_Loss:0.1550 ||Epoch_G_Loss:0.0924 ||Epoch_E_Loss:0.1101\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 281/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 281 || Epoch_D_Loss:0.1307 ||Epoch_G_Loss:0.1218 ||Epoch_E_Loss:0.1271\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 282/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 282 || Epoch_D_Loss:0.1129 ||Epoch_G_Loss:0.1211 ||Epoch_E_Loss:0.1440\n",
            "timer:  0.4255 sec.\n",
            "-------------\n",
            "Epoch 283/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 283 || Epoch_D_Loss:0.1090 ||Epoch_G_Loss:0.1259 ||Epoch_E_Loss:0.1489\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 284/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 284 || Epoch_D_Loss:0.1372 ||Epoch_G_Loss:0.1152 ||Epoch_E_Loss:0.1306\n",
            "timer:  0.4277 sec.\n",
            "-------------\n",
            "Epoch 285/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 285 || Epoch_D_Loss:0.1671 ||Epoch_G_Loss:0.0928 ||Epoch_E_Loss:0.1101\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 286/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 286 || Epoch_D_Loss:0.1910 ||Epoch_G_Loss:0.0730 ||Epoch_E_Loss:0.0930\n",
            "timer:  0.4247 sec.\n",
            "-------------\n",
            "Epoch 287/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 287 || Epoch_D_Loss:0.1707 ||Epoch_G_Loss:0.1000 ||Epoch_E_Loss:0.1056\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 288/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 288 || Epoch_D_Loss:0.1140 ||Epoch_G_Loss:0.1229 ||Epoch_E_Loss:0.1441\n",
            "timer:  0.4248 sec.\n",
            "-------------\n",
            "Epoch 289/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 289 || Epoch_D_Loss:0.1004 ||Epoch_G_Loss:0.1366 ||Epoch_E_Loss:0.1573\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 290/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 290 || Epoch_D_Loss:0.1250 ||Epoch_G_Loss:0.1222 ||Epoch_E_Loss:0.1359\n",
            "timer:  0.4281 sec.\n",
            "-------------\n",
            "Epoch 291/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 291 || Epoch_D_Loss:0.1688 ||Epoch_G_Loss:0.0964 ||Epoch_E_Loss:0.1097\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 292/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 292 || Epoch_D_Loss:0.1835 ||Epoch_G_Loss:0.0806 ||Epoch_E_Loss:0.1026\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 293/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 293 || Epoch_D_Loss:0.1635 ||Epoch_G_Loss:0.0869 ||Epoch_E_Loss:0.1155\n",
            "timer:  0.4284 sec.\n",
            "-------------\n",
            "Epoch 294/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 294 || Epoch_D_Loss:0.1347 ||Epoch_G_Loss:0.1196 ||Epoch_E_Loss:0.1274\n",
            "timer:  0.4285 sec.\n",
            "-------------\n",
            "Epoch 295/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 295 || Epoch_D_Loss:0.1167 ||Epoch_G_Loss:0.1158 ||Epoch_E_Loss:0.1430\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 296/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 296 || Epoch_D_Loss:0.1286 ||Epoch_G_Loss:0.1070 ||Epoch_E_Loss:0.1323\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 297/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 297 || Epoch_D_Loss:0.1409 ||Epoch_G_Loss:0.1128 ||Epoch_E_Loss:0.1223\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 298/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 298 || Epoch_D_Loss:0.1465 ||Epoch_G_Loss:0.1082 ||Epoch_E_Loss:0.1227\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 299/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 299 || Epoch_D_Loss:0.1387 ||Epoch_G_Loss:0.1215 ||Epoch_E_Loss:0.1289\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 300/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 300 || Epoch_D_Loss:0.1274 ||Epoch_G_Loss:0.1210 ||Epoch_E_Loss:0.1350\n",
            "timer:  0.4258 sec.\n",
            "-------------\n",
            "Epoch 301/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 301 || Epoch_D_Loss:0.1237 ||Epoch_G_Loss:0.1312 ||Epoch_E_Loss:0.1412\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 302/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 302 || Epoch_D_Loss:0.1440 ||Epoch_G_Loss:0.1094 ||Epoch_E_Loss:0.1249\n",
            "timer:  0.4275 sec.\n",
            "-------------\n",
            "Epoch 303/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 303 || Epoch_D_Loss:0.1651 ||Epoch_G_Loss:0.0892 ||Epoch_E_Loss:0.1189\n",
            "timer:  0.4280 sec.\n",
            "-------------\n",
            "Epoch 304/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 304 || Epoch_D_Loss:0.1911 ||Epoch_G_Loss:0.0874 ||Epoch_E_Loss:0.1065\n",
            "timer:  0.4270 sec.\n",
            "-------------\n",
            "Epoch 305/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 305 || Epoch_D_Loss:0.1614 ||Epoch_G_Loss:0.0962 ||Epoch_E_Loss:0.1238\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 306/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 306 || Epoch_D_Loss:0.1424 ||Epoch_G_Loss:0.1112 ||Epoch_E_Loss:0.1350\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 307/1500\n",
            "-------------\n",
            "（train）\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "-------------\n",
            "epoch 307 || Epoch_D_Loss:0.1501 ||Epoch_G_Loss:0.0967 ||Epoch_E_Loss:0.1281\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 308/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 308 || Epoch_D_Loss:0.1863 ||Epoch_G_Loss:0.0861 ||Epoch_E_Loss:0.1052\n",
            "timer:  0.4249 sec.\n",
            "-------------\n",
            "Epoch 309/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 309 || Epoch_D_Loss:0.1618 ||Epoch_G_Loss:0.1051 ||Epoch_E_Loss:0.1151\n",
            "timer:  0.4258 sec.\n",
            "-------------\n",
            "Epoch 310/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 310 || Epoch_D_Loss:0.1370 ||Epoch_G_Loss:0.1150 ||Epoch_E_Loss:0.1318\n",
            "timer:  0.4271 sec.\n",
            "-------------\n",
            "Epoch 311/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 311 || Epoch_D_Loss:0.1516 ||Epoch_G_Loss:0.0973 ||Epoch_E_Loss:0.1186\n",
            "timer:  0.4291 sec.\n",
            "-------------\n",
            "Epoch 312/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 312 || Epoch_D_Loss:0.1473 ||Epoch_G_Loss:0.0925 ||Epoch_E_Loss:0.1175\n",
            "timer:  0.4293 sec.\n",
            "-------------\n",
            "Epoch 313/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 313 || Epoch_D_Loss:0.1767 ||Epoch_G_Loss:0.0859 ||Epoch_E_Loss:0.0976\n",
            "timer:  0.4276 sec.\n",
            "-------------\n",
            "Epoch 314/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 314 || Epoch_D_Loss:0.1887 ||Epoch_G_Loss:0.0799 ||Epoch_E_Loss:0.0920\n",
            "timer:  0.4246 sec.\n",
            "-------------\n",
            "Epoch 315/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 315 || Epoch_D_Loss:0.1719 ||Epoch_G_Loss:0.0913 ||Epoch_E_Loss:0.1005\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 316/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 316 || Epoch_D_Loss:0.1308 ||Epoch_G_Loss:0.1148 ||Epoch_E_Loss:0.1247\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 317/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 317 || Epoch_D_Loss:0.1148 ||Epoch_G_Loss:0.1226 ||Epoch_E_Loss:0.1359\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 318/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 318 || Epoch_D_Loss:0.1329 ||Epoch_G_Loss:0.1180 ||Epoch_E_Loss:0.1277\n",
            "timer:  0.4250 sec.\n",
            "-------------\n",
            "Epoch 319/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 319 || Epoch_D_Loss:0.1627 ||Epoch_G_Loss:0.1046 ||Epoch_E_Loss:0.1095\n",
            "timer:  0.4252 sec.\n",
            "-------------\n",
            "Epoch 320/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 320 || Epoch_D_Loss:0.1648 ||Epoch_G_Loss:0.0818 ||Epoch_E_Loss:0.1127\n",
            "timer:  0.4275 sec.\n",
            "-------------\n",
            "Epoch 321/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 321 || Epoch_D_Loss:0.1594 ||Epoch_G_Loss:0.1048 ||Epoch_E_Loss:0.1088\n",
            "timer:  0.4276 sec.\n",
            "-------------\n",
            "Epoch 322/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 322 || Epoch_D_Loss:0.1441 ||Epoch_G_Loss:0.1094 ||Epoch_E_Loss:0.1220\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 323/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 323 || Epoch_D_Loss:0.1371 ||Epoch_G_Loss:0.1017 ||Epoch_E_Loss:0.1242\n",
            "timer:  0.4248 sec.\n",
            "-------------\n",
            "Epoch 324/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 324 || Epoch_D_Loss:0.1294 ||Epoch_G_Loss:0.1251 ||Epoch_E_Loss:0.1320\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 325/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 325 || Epoch_D_Loss:0.1177 ||Epoch_G_Loss:0.1360 ||Epoch_E_Loss:0.1406\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 326/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 326 || Epoch_D_Loss:0.1300 ||Epoch_G_Loss:0.1248 ||Epoch_E_Loss:0.1358\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 327/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 327 || Epoch_D_Loss:0.1683 ||Epoch_G_Loss:0.0918 ||Epoch_E_Loss:0.1193\n",
            "timer:  0.4280 sec.\n",
            "-------------\n",
            "Epoch 328/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 328 || Epoch_D_Loss:0.2227 ||Epoch_G_Loss:0.0713 ||Epoch_E_Loss:0.0917\n",
            "timer:  0.4304 sec.\n",
            "-------------\n",
            "Epoch 329/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 329 || Epoch_D_Loss:0.1865 ||Epoch_G_Loss:0.1002 ||Epoch_E_Loss:0.1071\n",
            "timer:  0.4308 sec.\n",
            "-------------\n",
            "Epoch 330/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 330 || Epoch_D_Loss:0.1495 ||Epoch_G_Loss:0.1143 ||Epoch_E_Loss:0.1272\n",
            "timer:  0.4309 sec.\n",
            "-------------\n",
            "Epoch 331/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 331 || Epoch_D_Loss:0.1489 ||Epoch_G_Loss:0.0987 ||Epoch_E_Loss:0.1260\n",
            "timer:  0.4301 sec.\n",
            "-------------\n",
            "Epoch 332/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 332 || Epoch_D_Loss:0.1875 ||Epoch_G_Loss:0.0863 ||Epoch_E_Loss:0.0991\n",
            "timer:  0.4309 sec.\n",
            "-------------\n",
            "Epoch 333/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 333 || Epoch_D_Loss:0.1742 ||Epoch_G_Loss:0.0868 ||Epoch_E_Loss:0.1067\n",
            "timer:  0.4283 sec.\n",
            "-------------\n",
            "Epoch 334/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 334 || Epoch_D_Loss:0.1681 ||Epoch_G_Loss:0.0909 ||Epoch_E_Loss:0.1081\n",
            "timer:  0.4290 sec.\n",
            "-------------\n",
            "Epoch 335/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 335 || Epoch_D_Loss:0.1739 ||Epoch_G_Loss:0.0912 ||Epoch_E_Loss:0.1052\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 336/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 336 || Epoch_D_Loss:0.1957 ||Epoch_G_Loss:0.0831 ||Epoch_E_Loss:0.0941\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 337/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 337 || Epoch_D_Loss:0.2000 ||Epoch_G_Loss:0.0793 ||Epoch_E_Loss:0.0923\n",
            "timer:  0.4275 sec.\n",
            "-------------\n",
            "Epoch 338/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 338 || Epoch_D_Loss:0.1646 ||Epoch_G_Loss:0.0918 ||Epoch_E_Loss:0.1080\n",
            "timer:  0.4276 sec.\n",
            "-------------\n",
            "Epoch 339/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 339 || Epoch_D_Loss:0.1330 ||Epoch_G_Loss:0.1091 ||Epoch_E_Loss:0.1270\n",
            "timer:  0.4285 sec.\n",
            "-------------\n",
            "Epoch 340/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 340 || Epoch_D_Loss:0.1286 ||Epoch_G_Loss:0.1084 ||Epoch_E_Loss:0.1308\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 341/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 341 || Epoch_D_Loss:0.1342 ||Epoch_G_Loss:0.1082 ||Epoch_E_Loss:0.1289\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 342/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 342 || Epoch_D_Loss:0.1389 ||Epoch_G_Loss:0.1224 ||Epoch_E_Loss:0.1271\n",
            "timer:  0.4270 sec.\n",
            "-------------\n",
            "Epoch 343/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 343 || Epoch_D_Loss:0.1485 ||Epoch_G_Loss:0.1074 ||Epoch_E_Loss:0.1255\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 344/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 344 || Epoch_D_Loss:0.1927 ||Epoch_G_Loss:0.0722 ||Epoch_E_Loss:0.0997\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 345/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 345 || Epoch_D_Loss:0.2182 ||Epoch_G_Loss:0.0646 ||Epoch_E_Loss:0.0851\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 346/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 346 || Epoch_D_Loss:0.1717 ||Epoch_G_Loss:0.0904 ||Epoch_E_Loss:0.1040\n",
            "timer:  0.4279 sec.\n",
            "-------------\n",
            "Epoch 347/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 347 || Epoch_D_Loss:0.1366 ||Epoch_G_Loss:0.1062 ||Epoch_E_Loss:0.1258\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 348/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 348 || Epoch_D_Loss:0.1461 ||Epoch_G_Loss:0.1008 ||Epoch_E_Loss:0.1152\n",
            "timer:  0.4276 sec.\n",
            "-------------\n",
            "Epoch 349/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 349 || Epoch_D_Loss:0.1571 ||Epoch_G_Loss:0.0864 ||Epoch_E_Loss:0.1141\n",
            "timer:  0.4275 sec.\n",
            "-------------\n",
            "Epoch 350/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 350 || Epoch_D_Loss:0.1403 ||Epoch_G_Loss:0.1005 ||Epoch_E_Loss:0.1241\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 351/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 351 || Epoch_D_Loss:0.1368 ||Epoch_G_Loss:0.1137 ||Epoch_E_Loss:0.1211\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 352/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 352 || Epoch_D_Loss:0.1486 ||Epoch_G_Loss:0.0978 ||Epoch_E_Loss:0.1121\n",
            "timer:  0.4287 sec.\n",
            "-------------\n",
            "Epoch 353/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 353 || Epoch_D_Loss:0.1422 ||Epoch_G_Loss:0.0959 ||Epoch_E_Loss:0.1173\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 354/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 354 || Epoch_D_Loss:0.1464 ||Epoch_G_Loss:0.1015 ||Epoch_E_Loss:0.1101\n",
            "timer:  0.4271 sec.\n",
            "-------------\n",
            "Epoch 355/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 355 || Epoch_D_Loss:0.1828 ||Epoch_G_Loss:0.0786 ||Epoch_E_Loss:0.0928\n",
            "timer:  0.4273 sec.\n",
            "-------------\n",
            "Epoch 356/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 356 || Epoch_D_Loss:0.2042 ||Epoch_G_Loss:0.0734 ||Epoch_E_Loss:0.0803\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 357/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 357 || Epoch_D_Loss:0.1784 ||Epoch_G_Loss:0.0784 ||Epoch_E_Loss:0.0938\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 358/1500\n",
            "-------------\n",
            "（train）\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "-------------\n",
            "epoch 358 || Epoch_D_Loss:0.1502 ||Epoch_G_Loss:0.0974 ||Epoch_E_Loss:0.1050\n",
            "timer:  0.4295 sec.\n",
            "-------------\n",
            "Epoch 359/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 359 || Epoch_D_Loss:0.1267 ||Epoch_G_Loss:0.1025 ||Epoch_E_Loss:0.1244\n",
            "timer:  0.4300 sec.\n",
            "-------------\n",
            "Epoch 360/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 360 || Epoch_D_Loss:0.1280 ||Epoch_G_Loss:0.1102 ||Epoch_E_Loss:0.1206\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 361/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 361 || Epoch_D_Loss:0.1341 ||Epoch_G_Loss:0.1043 ||Epoch_E_Loss:0.1160\n",
            "timer:  0.4277 sec.\n",
            "-------------\n",
            "Epoch 362/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 362 || Epoch_D_Loss:0.1337 ||Epoch_G_Loss:0.0994 ||Epoch_E_Loss:0.1205\n",
            "timer:  0.4271 sec.\n",
            "-------------\n",
            "Epoch 363/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 363 || Epoch_D_Loss:0.1461 ||Epoch_G_Loss:0.1027 ||Epoch_E_Loss:0.1090\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 364/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 364 || Epoch_D_Loss:0.1484 ||Epoch_G_Loss:0.0983 ||Epoch_E_Loss:0.1099\n",
            "timer:  0.4309 sec.\n",
            "-------------\n",
            "Epoch 365/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 365 || Epoch_D_Loss:0.1515 ||Epoch_G_Loss:0.0890 ||Epoch_E_Loss:0.1062\n",
            "timer:  0.4287 sec.\n",
            "-------------\n",
            "Epoch 366/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 366 || Epoch_D_Loss:0.1591 ||Epoch_G_Loss:0.0922 ||Epoch_E_Loss:0.0992\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 367/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 367 || Epoch_D_Loss:0.1516 ||Epoch_G_Loss:0.0862 ||Epoch_E_Loss:0.1042\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 368/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 368 || Epoch_D_Loss:0.1594 ||Epoch_G_Loss:0.0921 ||Epoch_E_Loss:0.0962\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 369/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 369 || Epoch_D_Loss:0.1427 ||Epoch_G_Loss:0.0921 ||Epoch_E_Loss:0.1103\n",
            "timer:  0.4277 sec.\n",
            "-------------\n",
            "Epoch 370/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 370 || Epoch_D_Loss:0.1447 ||Epoch_G_Loss:0.0960 ||Epoch_E_Loss:0.1079\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 371/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 371 || Epoch_D_Loss:0.1377 ||Epoch_G_Loss:0.0936 ||Epoch_E_Loss:0.1184\n",
            "timer:  0.4252 sec.\n",
            "-------------\n",
            "Epoch 372/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 372 || Epoch_D_Loss:0.1299 ||Epoch_G_Loss:0.1118 ||Epoch_E_Loss:0.1225\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 373/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 373 || Epoch_D_Loss:0.1225 ||Epoch_G_Loss:0.1135 ||Epoch_E_Loss:0.1293\n",
            "timer:  0.4285 sec.\n",
            "-------------\n",
            "Epoch 374/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 374 || Epoch_D_Loss:0.1290 ||Epoch_G_Loss:0.1078 ||Epoch_E_Loss:0.1239\n",
            "timer:  0.4276 sec.\n",
            "-------------\n",
            "Epoch 375/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 375 || Epoch_D_Loss:0.1542 ||Epoch_G_Loss:0.0899 ||Epoch_E_Loss:0.1064\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 376/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 376 || Epoch_D_Loss:0.1670 ||Epoch_G_Loss:0.0811 ||Epoch_E_Loss:0.0997\n",
            "timer:  0.4278 sec.\n",
            "-------------\n",
            "Epoch 377/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 377 || Epoch_D_Loss:0.1664 ||Epoch_G_Loss:0.0839 ||Epoch_E_Loss:0.0963\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 378/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 378 || Epoch_D_Loss:0.1768 ||Epoch_G_Loss:0.0748 ||Epoch_E_Loss:0.0946\n",
            "timer:  0.4282 sec.\n",
            "-------------\n",
            "Epoch 379/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 379 || Epoch_D_Loss:0.1793 ||Epoch_G_Loss:0.0702 ||Epoch_E_Loss:0.0913\n",
            "timer:  0.4282 sec.\n",
            "-------------\n",
            "Epoch 380/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 380 || Epoch_D_Loss:0.1817 ||Epoch_G_Loss:0.0705 ||Epoch_E_Loss:0.0934\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 381/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 381 || Epoch_D_Loss:0.1587 ||Epoch_G_Loss:0.0905 ||Epoch_E_Loss:0.1003\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 382/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 382 || Epoch_D_Loss:0.1485 ||Epoch_G_Loss:0.0945 ||Epoch_E_Loss:0.1098\n",
            "timer:  0.4280 sec.\n",
            "-------------\n",
            "Epoch 383/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 383 || Epoch_D_Loss:0.1356 ||Epoch_G_Loss:0.0953 ||Epoch_E_Loss:0.1203\n",
            "timer:  0.4284 sec.\n",
            "-------------\n",
            "Epoch 384/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 384 || Epoch_D_Loss:0.1347 ||Epoch_G_Loss:0.1146 ||Epoch_E_Loss:0.1176\n",
            "timer:  0.4277 sec.\n",
            "-------------\n",
            "Epoch 385/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 385 || Epoch_D_Loss:0.1356 ||Epoch_G_Loss:0.1093 ||Epoch_E_Loss:0.1174\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 386/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 386 || Epoch_D_Loss:0.1298 ||Epoch_G_Loss:0.1106 ||Epoch_E_Loss:0.1208\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 387/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 387 || Epoch_D_Loss:0.1438 ||Epoch_G_Loss:0.0982 ||Epoch_E_Loss:0.1133\n",
            "timer:  0.4245 sec.\n",
            "-------------\n",
            "Epoch 388/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 388 || Epoch_D_Loss:0.1572 ||Epoch_G_Loss:0.0963 ||Epoch_E_Loss:0.1034\n",
            "timer:  0.4276 sec.\n",
            "-------------\n",
            "Epoch 389/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 389 || Epoch_D_Loss:0.1654 ||Epoch_G_Loss:0.0924 ||Epoch_E_Loss:0.0958\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 390/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 390 || Epoch_D_Loss:0.1617 ||Epoch_G_Loss:0.0854 ||Epoch_E_Loss:0.0980\n",
            "timer:  0.4295 sec.\n",
            "-------------\n",
            "Epoch 391/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 391 || Epoch_D_Loss:0.1628 ||Epoch_G_Loss:0.0855 ||Epoch_E_Loss:0.1014\n",
            "timer:  0.4287 sec.\n",
            "-------------\n",
            "Epoch 392/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 392 || Epoch_D_Loss:0.1633 ||Epoch_G_Loss:0.0904 ||Epoch_E_Loss:0.1006\n",
            "timer:  0.4310 sec.\n",
            "-------------\n",
            "Epoch 393/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 393 || Epoch_D_Loss:0.1583 ||Epoch_G_Loss:0.0878 ||Epoch_E_Loss:0.1063\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 394/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 394 || Epoch_D_Loss:0.1614 ||Epoch_G_Loss:0.0941 ||Epoch_E_Loss:0.1004\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 395/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 395 || Epoch_D_Loss:0.1638 ||Epoch_G_Loss:0.0852 ||Epoch_E_Loss:0.1038\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 396/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 396 || Epoch_D_Loss:0.1589 ||Epoch_G_Loss:0.0888 ||Epoch_E_Loss:0.1042\n",
            "timer:  0.4279 sec.\n",
            "-------------\n",
            "Epoch 397/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 397 || Epoch_D_Loss:0.1593 ||Epoch_G_Loss:0.0829 ||Epoch_E_Loss:0.1063\n",
            "timer:  0.4304 sec.\n",
            "-------------\n",
            "Epoch 398/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 398 || Epoch_D_Loss:0.1576 ||Epoch_G_Loss:0.0805 ||Epoch_E_Loss:0.1058\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 399/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 399 || Epoch_D_Loss:0.1517 ||Epoch_G_Loss:0.0910 ||Epoch_E_Loss:0.1061\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 400/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 400 || Epoch_D_Loss:0.1389 ||Epoch_G_Loss:0.1052 ||Epoch_E_Loss:0.1128\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 401/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 401 || Epoch_D_Loss:0.1536 ||Epoch_G_Loss:0.0940 ||Epoch_E_Loss:0.1054\n",
            "timer:  0.4270 sec.\n",
            "-------------\n",
            "Epoch 402/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 402 || Epoch_D_Loss:0.1713 ||Epoch_G_Loss:0.0871 ||Epoch_E_Loss:0.0950\n",
            "timer:  0.4271 sec.\n",
            "-------------\n",
            "Epoch 403/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 403 || Epoch_D_Loss:0.1480 ||Epoch_G_Loss:0.0947 ||Epoch_E_Loss:0.1057\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 404/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 404 || Epoch_D_Loss:0.1379 ||Epoch_G_Loss:0.0924 ||Epoch_E_Loss:0.1121\n",
            "timer:  0.4289 sec.\n",
            "-------------\n",
            "Epoch 405/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 405 || Epoch_D_Loss:0.1457 ||Epoch_G_Loss:0.1018 ||Epoch_E_Loss:0.1040\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 406/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 406 || Epoch_D_Loss:0.1381 ||Epoch_G_Loss:0.1008 ||Epoch_E_Loss:0.1137\n",
            "timer:  0.4255 sec.\n",
            "-------------\n",
            "Epoch 407/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 407 || Epoch_D_Loss:0.1264 ||Epoch_G_Loss:0.1037 ||Epoch_E_Loss:0.1215\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 408/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 408 || Epoch_D_Loss:0.1247 ||Epoch_G_Loss:0.1068 ||Epoch_E_Loss:0.1208\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 409/1500\n",
            "-------------\n",
            "（train）\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "-------------\n",
            "epoch 409 || Epoch_D_Loss:0.1394 ||Epoch_G_Loss:0.0945 ||Epoch_E_Loss:0.1105\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 410/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 410 || Epoch_D_Loss:0.1600 ||Epoch_G_Loss:0.0958 ||Epoch_E_Loss:0.0967\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 411/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 411 || Epoch_D_Loss:0.1463 ||Epoch_G_Loss:0.0956 ||Epoch_E_Loss:0.1072\n",
            "timer:  0.4279 sec.\n",
            "-------------\n",
            "Epoch 412/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 412 || Epoch_D_Loss:0.1381 ||Epoch_G_Loss:0.0934 ||Epoch_E_Loss:0.1122\n",
            "timer:  0.4284 sec.\n",
            "-------------\n",
            "Epoch 413/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 413 || Epoch_D_Loss:0.1543 ||Epoch_G_Loss:0.0921 ||Epoch_E_Loss:0.0969\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 414/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 414 || Epoch_D_Loss:0.1706 ||Epoch_G_Loss:0.0800 ||Epoch_E_Loss:0.0927\n",
            "timer:  0.4250 sec.\n",
            "-------------\n",
            "Epoch 415/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 415 || Epoch_D_Loss:0.1584 ||Epoch_G_Loss:0.0879 ||Epoch_E_Loss:0.0952\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 416/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 416 || Epoch_D_Loss:0.1376 ||Epoch_G_Loss:0.1020 ||Epoch_E_Loss:0.1062\n",
            "timer:  0.4270 sec.\n",
            "-------------\n",
            "Epoch 417/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 417 || Epoch_D_Loss:0.1218 ||Epoch_G_Loss:0.1030 ||Epoch_E_Loss:0.1203\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 418/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 418 || Epoch_D_Loss:0.1220 ||Epoch_G_Loss:0.1137 ||Epoch_E_Loss:0.1153\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 419/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 419 || Epoch_D_Loss:0.1319 ||Epoch_G_Loss:0.0969 ||Epoch_E_Loss:0.1134\n",
            "timer:  0.4270 sec.\n",
            "-------------\n",
            "Epoch 420/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 420 || Epoch_D_Loss:0.1472 ||Epoch_G_Loss:0.1070 ||Epoch_E_Loss:0.1004\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 421/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 421 || Epoch_D_Loss:0.1457 ||Epoch_G_Loss:0.0978 ||Epoch_E_Loss:0.1035\n",
            "timer:  0.4275 sec.\n",
            "-------------\n",
            "Epoch 422/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 422 || Epoch_D_Loss:0.1450 ||Epoch_G_Loss:0.0903 ||Epoch_E_Loss:0.1069\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 423/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 423 || Epoch_D_Loss:0.1576 ||Epoch_G_Loss:0.0912 ||Epoch_E_Loss:0.0937\n",
            "timer:  0.4240 sec.\n",
            "-------------\n",
            "Epoch 424/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 424 || Epoch_D_Loss:0.1568 ||Epoch_G_Loss:0.0895 ||Epoch_E_Loss:0.0957\n",
            "timer:  0.4253 sec.\n",
            "-------------\n",
            "Epoch 425/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 425 || Epoch_D_Loss:0.1487 ||Epoch_G_Loss:0.0979 ||Epoch_E_Loss:0.1019\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 426/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 426 || Epoch_D_Loss:0.1269 ||Epoch_G_Loss:0.0994 ||Epoch_E_Loss:0.1169\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 427/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 427 || Epoch_D_Loss:0.1226 ||Epoch_G_Loss:0.1105 ||Epoch_E_Loss:0.1183\n",
            "timer:  0.4271 sec.\n",
            "-------------\n",
            "Epoch 428/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 428 || Epoch_D_Loss:0.1208 ||Epoch_G_Loss:0.1077 ||Epoch_E_Loss:0.1222\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 429/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 429 || Epoch_D_Loss:0.1291 ||Epoch_G_Loss:0.1108 ||Epoch_E_Loss:0.1149\n",
            "timer:  0.4258 sec.\n",
            "-------------\n",
            "Epoch 430/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 430 || Epoch_D_Loss:0.1248 ||Epoch_G_Loss:0.1026 ||Epoch_E_Loss:0.1214\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 431/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 431 || Epoch_D_Loss:0.1409 ||Epoch_G_Loss:0.0982 ||Epoch_E_Loss:0.1102\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 432/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 432 || Epoch_D_Loss:0.1416 ||Epoch_G_Loss:0.0932 ||Epoch_E_Loss:0.1109\n",
            "timer:  0.4247 sec.\n",
            "-------------\n",
            "Epoch 433/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 433 || Epoch_D_Loss:0.1599 ||Epoch_G_Loss:0.0867 ||Epoch_E_Loss:0.1034\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 434/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 434 || Epoch_D_Loss:0.1633 ||Epoch_G_Loss:0.0843 ||Epoch_E_Loss:0.0987\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 435/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 435 || Epoch_D_Loss:0.1534 ||Epoch_G_Loss:0.0959 ||Epoch_E_Loss:0.1012\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 436/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 436 || Epoch_D_Loss:0.1420 ||Epoch_G_Loss:0.0958 ||Epoch_E_Loss:0.1143\n",
            "timer:  0.4276 sec.\n",
            "-------------\n",
            "Epoch 437/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 437 || Epoch_D_Loss:0.1343 ||Epoch_G_Loss:0.1102 ||Epoch_E_Loss:0.1144\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 438/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 438 || Epoch_D_Loss:0.1365 ||Epoch_G_Loss:0.0948 ||Epoch_E_Loss:0.1229\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 439/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 439 || Epoch_D_Loss:0.1376 ||Epoch_G_Loss:0.1040 ||Epoch_E_Loss:0.1197\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 440/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 440 || Epoch_D_Loss:0.1414 ||Epoch_G_Loss:0.1100 ||Epoch_E_Loss:0.1131\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 441/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 441 || Epoch_D_Loss:0.1382 ||Epoch_G_Loss:0.1224 ||Epoch_E_Loss:0.1191\n",
            "timer:  0.4245 sec.\n",
            "-------------\n",
            "Epoch 442/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 442 || Epoch_D_Loss:0.1397 ||Epoch_G_Loss:0.1092 ||Epoch_E_Loss:0.1226\n",
            "timer:  0.4270 sec.\n",
            "-------------\n",
            "Epoch 443/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 443 || Epoch_D_Loss:0.1489 ||Epoch_G_Loss:0.1023 ||Epoch_E_Loss:0.1158\n",
            "timer:  0.4275 sec.\n",
            "-------------\n",
            "Epoch 444/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 444 || Epoch_D_Loss:0.1607 ||Epoch_G_Loss:0.0955 ||Epoch_E_Loss:0.1047\n",
            "timer:  0.4288 sec.\n",
            "-------------\n",
            "Epoch 445/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 445 || Epoch_D_Loss:0.1532 ||Epoch_G_Loss:0.0985 ||Epoch_E_Loss:0.1129\n",
            "timer:  0.4284 sec.\n",
            "-------------\n",
            "Epoch 446/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 446 || Epoch_D_Loss:0.1659 ||Epoch_G_Loss:0.0899 ||Epoch_E_Loss:0.1071\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 447/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 447 || Epoch_D_Loss:0.1746 ||Epoch_G_Loss:0.0845 ||Epoch_E_Loss:0.0965\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 448/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 448 || Epoch_D_Loss:0.1634 ||Epoch_G_Loss:0.0984 ||Epoch_E_Loss:0.1073\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 449/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 449 || Epoch_D_Loss:0.1436 ||Epoch_G_Loss:0.0933 ||Epoch_E_Loss:0.1184\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 450/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 450 || Epoch_D_Loss:0.1490 ||Epoch_G_Loss:0.1024 ||Epoch_E_Loss:0.1133\n",
            "timer:  0.4258 sec.\n",
            "-------------\n",
            "Epoch 451/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 451 || Epoch_D_Loss:0.1511 ||Epoch_G_Loss:0.0894 ||Epoch_E_Loss:0.1126\n",
            "timer:  0.4255 sec.\n",
            "-------------\n",
            "Epoch 452/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 452 || Epoch_D_Loss:0.1688 ||Epoch_G_Loss:0.0732 ||Epoch_E_Loss:0.1084\n",
            "timer:  0.4254 sec.\n",
            "-------------\n",
            "Epoch 453/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 453 || Epoch_D_Loss:0.1768 ||Epoch_G_Loss:0.0919 ||Epoch_E_Loss:0.1000\n",
            "timer:  0.4258 sec.\n",
            "-------------\n",
            "Epoch 454/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 454 || Epoch_D_Loss:0.1520 ||Epoch_G_Loss:0.0911 ||Epoch_E_Loss:0.1162\n",
            "timer:  0.4283 sec.\n",
            "-------------\n",
            "Epoch 455/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 455 || Epoch_D_Loss:0.1598 ||Epoch_G_Loss:0.0963 ||Epoch_E_Loss:0.1089\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 456/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 456 || Epoch_D_Loss:0.1762 ||Epoch_G_Loss:0.0732 ||Epoch_E_Loss:0.1001\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 457/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 457 || Epoch_D_Loss:0.2177 ||Epoch_G_Loss:0.0665 ||Epoch_E_Loss:0.0811\n",
            "timer:  0.4252 sec.\n",
            "-------------\n",
            "Epoch 458/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 458 || Epoch_D_Loss:0.1949 ||Epoch_G_Loss:0.0723 ||Epoch_E_Loss:0.0926\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 459/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 459 || Epoch_D_Loss:0.1614 ||Epoch_G_Loss:0.0875 ||Epoch_E_Loss:0.1070\n",
            "timer:  0.4252 sec.\n",
            "-------------\n",
            "Epoch 460/1500\n",
            "-------------\n",
            "（train）\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "-------------\n",
            "epoch 460 || Epoch_D_Loss:0.1503 ||Epoch_G_Loss:0.1036 ||Epoch_E_Loss:0.1127\n",
            "timer:  0.4232 sec.\n",
            "-------------\n",
            "Epoch 461/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 461 || Epoch_D_Loss:0.1617 ||Epoch_G_Loss:0.0944 ||Epoch_E_Loss:0.1078\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 462/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 462 || Epoch_D_Loss:0.1647 ||Epoch_G_Loss:0.0844 ||Epoch_E_Loss:0.1038\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 463/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 463 || Epoch_D_Loss:0.1697 ||Epoch_G_Loss:0.0838 ||Epoch_E_Loss:0.0986\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 464/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 464 || Epoch_D_Loss:0.1739 ||Epoch_G_Loss:0.0770 ||Epoch_E_Loss:0.0981\n",
            "timer:  0.4280 sec.\n",
            "-------------\n",
            "Epoch 465/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 465 || Epoch_D_Loss:0.1912 ||Epoch_G_Loss:0.0713 ||Epoch_E_Loss:0.0847\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 466/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 466 || Epoch_D_Loss:0.1889 ||Epoch_G_Loss:0.0731 ||Epoch_E_Loss:0.0853\n",
            "timer:  0.4270 sec.\n",
            "-------------\n",
            "Epoch 467/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 467 || Epoch_D_Loss:0.1694 ||Epoch_G_Loss:0.0823 ||Epoch_E_Loss:0.0932\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 468/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 468 || Epoch_D_Loss:0.1572 ||Epoch_G_Loss:0.0852 ||Epoch_E_Loss:0.0961\n",
            "timer:  0.4250 sec.\n",
            "-------------\n",
            "Epoch 469/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 469 || Epoch_D_Loss:0.1569 ||Epoch_G_Loss:0.0901 ||Epoch_E_Loss:0.0942\n",
            "timer:  0.4271 sec.\n",
            "-------------\n",
            "Epoch 470/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 470 || Epoch_D_Loss:0.1339 ||Epoch_G_Loss:0.0941 ||Epoch_E_Loss:0.1102\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 471/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 471 || Epoch_D_Loss:0.1269 ||Epoch_G_Loss:0.1038 ||Epoch_E_Loss:0.1134\n",
            "timer:  0.4278 sec.\n",
            "-------------\n",
            "Epoch 472/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 472 || Epoch_D_Loss:0.1449 ||Epoch_G_Loss:0.0850 ||Epoch_E_Loss:0.1054\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 473/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 473 || Epoch_D_Loss:0.1772 ||Epoch_G_Loss:0.0783 ||Epoch_E_Loss:0.0853\n",
            "timer:  0.4281 sec.\n",
            "-------------\n",
            "Epoch 474/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 474 || Epoch_D_Loss:0.2015 ||Epoch_G_Loss:0.0644 ||Epoch_E_Loss:0.0746\n",
            "timer:  0.4293 sec.\n",
            "-------------\n",
            "Epoch 475/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 475 || Epoch_D_Loss:0.1615 ||Epoch_G_Loss:0.0814 ||Epoch_E_Loss:0.0933\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 476/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 476 || Epoch_D_Loss:0.1261 ||Epoch_G_Loss:0.0992 ||Epoch_E_Loss:0.1107\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 477/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 477 || Epoch_D_Loss:0.1417 ||Epoch_G_Loss:0.0920 ||Epoch_E_Loss:0.1028\n",
            "timer:  0.4242 sec.\n",
            "-------------\n",
            "Epoch 478/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 478 || Epoch_D_Loss:0.1560 ||Epoch_G_Loss:0.0828 ||Epoch_E_Loss:0.0905\n",
            "timer:  0.4247 sec.\n",
            "-------------\n",
            "Epoch 479/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 479 || Epoch_D_Loss:0.1475 ||Epoch_G_Loss:0.0888 ||Epoch_E_Loss:0.0974\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 480/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 480 || Epoch_D_Loss:0.1166 ||Epoch_G_Loss:0.1086 ||Epoch_E_Loss:0.1157\n",
            "timer:  0.4310 sec.\n",
            "-------------\n",
            "Epoch 481/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 481 || Epoch_D_Loss:0.1089 ||Epoch_G_Loss:0.1104 ||Epoch_E_Loss:0.1215\n",
            "timer:  0.4258 sec.\n",
            "-------------\n",
            "Epoch 482/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 482 || Epoch_D_Loss:0.1275 ||Epoch_G_Loss:0.1007 ||Epoch_E_Loss:0.1077\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 483/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 483 || Epoch_D_Loss:0.1356 ||Epoch_G_Loss:0.0901 ||Epoch_E_Loss:0.1035\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 484/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 484 || Epoch_D_Loss:0.1276 ||Epoch_G_Loss:0.0984 ||Epoch_E_Loss:0.1077\n",
            "timer:  0.4249 sec.\n",
            "-------------\n",
            "Epoch 485/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 485 || Epoch_D_Loss:0.1256 ||Epoch_G_Loss:0.1059 ||Epoch_E_Loss:0.1113\n",
            "timer:  0.4238 sec.\n",
            "-------------\n",
            "Epoch 486/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 486 || Epoch_D_Loss:0.1347 ||Epoch_G_Loss:0.1030 ||Epoch_E_Loss:0.1055\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 487/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 487 || Epoch_D_Loss:0.1472 ||Epoch_G_Loss:0.0903 ||Epoch_E_Loss:0.1039\n",
            "timer:  0.4254 sec.\n",
            "-------------\n",
            "Epoch 488/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 488 || Epoch_D_Loss:0.1546 ||Epoch_G_Loss:0.0883 ||Epoch_E_Loss:0.0935\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 489/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 489 || Epoch_D_Loss:0.1446 ||Epoch_G_Loss:0.0900 ||Epoch_E_Loss:0.1024\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 490/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 490 || Epoch_D_Loss:0.1455 ||Epoch_G_Loss:0.0907 ||Epoch_E_Loss:0.1030\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 491/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 491 || Epoch_D_Loss:0.1564 ||Epoch_G_Loss:0.0881 ||Epoch_E_Loss:0.0995\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 492/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 492 || Epoch_D_Loss:0.1555 ||Epoch_G_Loss:0.0848 ||Epoch_E_Loss:0.0995\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 493/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 493 || Epoch_D_Loss:0.1372 ||Epoch_G_Loss:0.0967 ||Epoch_E_Loss:0.1070\n",
            "timer:  0.4252 sec.\n",
            "-------------\n",
            "Epoch 494/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 494 || Epoch_D_Loss:0.1257 ||Epoch_G_Loss:0.1030 ||Epoch_E_Loss:0.1185\n",
            "timer:  0.4235 sec.\n",
            "-------------\n",
            "Epoch 495/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 495 || Epoch_D_Loss:0.1238 ||Epoch_G_Loss:0.1065 ||Epoch_E_Loss:0.1188\n",
            "timer:  0.4243 sec.\n",
            "-------------\n",
            "Epoch 496/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 496 || Epoch_D_Loss:0.1213 ||Epoch_G_Loss:0.1056 ||Epoch_E_Loss:0.1208\n",
            "timer:  0.4251 sec.\n",
            "-------------\n",
            "Epoch 497/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 497 || Epoch_D_Loss:0.1070 ||Epoch_G_Loss:0.1135 ||Epoch_E_Loss:0.1358\n",
            "timer:  0.4283 sec.\n",
            "-------------\n",
            "Epoch 498/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 498 || Epoch_D_Loss:0.1273 ||Epoch_G_Loss:0.1117 ||Epoch_E_Loss:0.1162\n",
            "timer:  0.4288 sec.\n",
            "-------------\n",
            "Epoch 499/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 499 || Epoch_D_Loss:0.1549 ||Epoch_G_Loss:0.0878 ||Epoch_E_Loss:0.0974\n",
            "timer:  0.4279 sec.\n",
            "-------------\n",
            "Epoch 500/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 500 || Epoch_D_Loss:0.1890 ||Epoch_G_Loss:0.0687 ||Epoch_E_Loss:0.0803\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 501/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 501 || Epoch_D_Loss:0.1981 ||Epoch_G_Loss:0.0678 ||Epoch_E_Loss:0.0767\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 502/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 502 || Epoch_D_Loss:0.1755 ||Epoch_G_Loss:0.0751 ||Epoch_E_Loss:0.0856\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 503/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 503 || Epoch_D_Loss:0.1548 ||Epoch_G_Loss:0.0872 ||Epoch_E_Loss:0.0989\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 504/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 504 || Epoch_D_Loss:0.1373 ||Epoch_G_Loss:0.1014 ||Epoch_E_Loss:0.1070\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 505/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 505 || Epoch_D_Loss:0.1285 ||Epoch_G_Loss:0.1093 ||Epoch_E_Loss:0.1135\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 506/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 506 || Epoch_D_Loss:0.1128 ||Epoch_G_Loss:0.1205 ||Epoch_E_Loss:0.1281\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 507/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 507 || Epoch_D_Loss:0.1056 ||Epoch_G_Loss:0.1258 ||Epoch_E_Loss:0.1373\n",
            "timer:  0.4277 sec.\n",
            "-------------\n",
            "Epoch 508/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 508 || Epoch_D_Loss:0.1235 ||Epoch_G_Loss:0.1071 ||Epoch_E_Loss:0.1236\n",
            "timer:  0.4250 sec.\n",
            "-------------\n",
            "Epoch 509/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 509 || Epoch_D_Loss:0.1460 ||Epoch_G_Loss:0.0899 ||Epoch_E_Loss:0.1057\n",
            "timer:  0.4252 sec.\n",
            "-------------\n",
            "Epoch 510/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 510 || Epoch_D_Loss:0.1459 ||Epoch_G_Loss:0.0964 ||Epoch_E_Loss:0.1035\n",
            "timer:  0.4253 sec.\n",
            "-------------\n",
            "Epoch 511/1500\n",
            "-------------\n",
            "（train）\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "-------------\n",
            "epoch 511 || Epoch_D_Loss:0.1360 ||Epoch_G_Loss:0.0970 ||Epoch_E_Loss:0.1246\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 512/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 512 || Epoch_D_Loss:0.1400 ||Epoch_G_Loss:0.0930 ||Epoch_E_Loss:0.1075\n",
            "timer:  0.4253 sec.\n",
            "-------------\n",
            "Epoch 513/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 513 || Epoch_D_Loss:0.1558 ||Epoch_G_Loss:0.0853 ||Epoch_E_Loss:0.0963\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 514/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 514 || Epoch_D_Loss:0.1551 ||Epoch_G_Loss:0.0839 ||Epoch_E_Loss:0.0965\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 515/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 515 || Epoch_D_Loss:0.1537 ||Epoch_G_Loss:0.0940 ||Epoch_E_Loss:0.0986\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 516/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 516 || Epoch_D_Loss:0.1311 ||Epoch_G_Loss:0.0983 ||Epoch_E_Loss:0.1207\n",
            "timer:  0.4245 sec.\n",
            "-------------\n",
            "Epoch 517/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 517 || Epoch_D_Loss:0.1272 ||Epoch_G_Loss:0.1039 ||Epoch_E_Loss:0.1170\n",
            "timer:  0.4252 sec.\n",
            "-------------\n",
            "Epoch 518/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 518 || Epoch_D_Loss:0.1232 ||Epoch_G_Loss:0.1100 ||Epoch_E_Loss:0.1229\n",
            "timer:  0.4284 sec.\n",
            "-------------\n",
            "Epoch 519/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 519 || Epoch_D_Loss:0.1261 ||Epoch_G_Loss:0.1121 ||Epoch_E_Loss:0.1207\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 520/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 520 || Epoch_D_Loss:0.1139 ||Epoch_G_Loss:0.1131 ||Epoch_E_Loss:0.1322\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 521/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 521 || Epoch_D_Loss:0.1290 ||Epoch_G_Loss:0.1065 ||Epoch_E_Loss:0.1203\n",
            "timer:  0.4271 sec.\n",
            "-------------\n",
            "Epoch 522/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 522 || Epoch_D_Loss:0.1704 ||Epoch_G_Loss:0.0839 ||Epoch_E_Loss:0.0945\n",
            "timer:  0.4255 sec.\n",
            "-------------\n",
            "Epoch 523/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 523 || Epoch_D_Loss:0.1591 ||Epoch_G_Loss:0.0810 ||Epoch_E_Loss:0.1079\n",
            "timer:  0.4253 sec.\n",
            "-------------\n",
            "Epoch 524/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 524 || Epoch_D_Loss:0.1375 ||Epoch_G_Loss:0.1075 ||Epoch_E_Loss:0.1109\n",
            "timer:  0.4251 sec.\n",
            "-------------\n",
            "Epoch 525/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 525 || Epoch_D_Loss:0.1316 ||Epoch_G_Loss:0.1040 ||Epoch_E_Loss:0.1164\n",
            "timer:  0.4253 sec.\n",
            "-------------\n",
            "Epoch 526/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 526 || Epoch_D_Loss:0.1546 ||Epoch_G_Loss:0.0876 ||Epoch_E_Loss:0.1012\n",
            "timer:  0.4271 sec.\n",
            "-------------\n",
            "Epoch 527/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 527 || Epoch_D_Loss:0.1704 ||Epoch_G_Loss:0.0873 ||Epoch_E_Loss:0.0904\n",
            "timer:  0.4295 sec.\n",
            "-------------\n",
            "Epoch 528/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 528 || Epoch_D_Loss:0.1504 ||Epoch_G_Loss:0.0899 ||Epoch_E_Loss:0.1009\n",
            "timer:  0.4296 sec.\n",
            "-------------\n",
            "Epoch 529/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 529 || Epoch_D_Loss:0.1173 ||Epoch_G_Loss:0.1051 ||Epoch_E_Loss:0.1263\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 530/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 530 || Epoch_D_Loss:0.1003 ||Epoch_G_Loss:0.1286 ||Epoch_E_Loss:0.1373\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 531/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 531 || Epoch_D_Loss:0.1016 ||Epoch_G_Loss:0.1250 ||Epoch_E_Loss:0.1342\n",
            "timer:  0.4253 sec.\n",
            "-------------\n",
            "Epoch 532/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 532 || Epoch_D_Loss:0.1096 ||Epoch_G_Loss:0.1227 ||Epoch_E_Loss:0.1269\n",
            "timer:  0.4245 sec.\n",
            "-------------\n",
            "Epoch 533/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 533 || Epoch_D_Loss:0.1336 ||Epoch_G_Loss:0.0959 ||Epoch_E_Loss:0.1087\n",
            "timer:  0.4252 sec.\n",
            "-------------\n",
            "Epoch 534/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 534 || Epoch_D_Loss:0.1570 ||Epoch_G_Loss:0.0859 ||Epoch_E_Loss:0.0993\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 535/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 535 || Epoch_D_Loss:0.1671 ||Epoch_G_Loss:0.0836 ||Epoch_E_Loss:0.0906\n",
            "timer:  0.4258 sec.\n",
            "-------------\n",
            "Epoch 536/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 536 || Epoch_D_Loss:0.1561 ||Epoch_G_Loss:0.0875 ||Epoch_E_Loss:0.0988\n",
            "timer:  0.4255 sec.\n",
            "-------------\n",
            "Epoch 537/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 537 || Epoch_D_Loss:0.1324 ||Epoch_G_Loss:0.1029 ||Epoch_E_Loss:0.1092\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 538/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 538 || Epoch_D_Loss:0.1315 ||Epoch_G_Loss:0.1087 ||Epoch_E_Loss:0.1146\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 539/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 539 || Epoch_D_Loss:0.1269 ||Epoch_G_Loss:0.1017 ||Epoch_E_Loss:0.1185\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 540/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 540 || Epoch_D_Loss:0.1275 ||Epoch_G_Loss:0.1061 ||Epoch_E_Loss:0.1159\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 541/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 541 || Epoch_D_Loss:0.1161 ||Epoch_G_Loss:0.1227 ||Epoch_E_Loss:0.1207\n",
            "timer:  0.4239 sec.\n",
            "-------------\n",
            "Epoch 542/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 542 || Epoch_D_Loss:0.1078 ||Epoch_G_Loss:0.1177 ||Epoch_E_Loss:0.1365\n",
            "timer:  0.4252 sec.\n",
            "-------------\n",
            "Epoch 543/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 543 || Epoch_D_Loss:0.1132 ||Epoch_G_Loss:0.1150 ||Epoch_E_Loss:0.1278\n",
            "timer:  0.4251 sec.\n",
            "-------------\n",
            "Epoch 544/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 544 || Epoch_D_Loss:0.1292 ||Epoch_G_Loss:0.1072 ||Epoch_E_Loss:0.1151\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 545/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 545 || Epoch_D_Loss:0.1499 ||Epoch_G_Loss:0.0916 ||Epoch_E_Loss:0.1004\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 546/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 546 || Epoch_D_Loss:0.1638 ||Epoch_G_Loss:0.0869 ||Epoch_E_Loss:0.0894\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 547/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 547 || Epoch_D_Loss:0.1708 ||Epoch_G_Loss:0.0765 ||Epoch_E_Loss:0.0952\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 548/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 548 || Epoch_D_Loss:0.1558 ||Epoch_G_Loss:0.0852 ||Epoch_E_Loss:0.0955\n",
            "timer:  0.4291 sec.\n",
            "-------------\n",
            "Epoch 549/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 549 || Epoch_D_Loss:0.1428 ||Epoch_G_Loss:0.1014 ||Epoch_E_Loss:0.1053\n",
            "timer:  0.4285 sec.\n",
            "-------------\n",
            "Epoch 550/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 550 || Epoch_D_Loss:0.1250 ||Epoch_G_Loss:0.1054 ||Epoch_E_Loss:0.1187\n",
            "timer:  0.4258 sec.\n",
            "-------------\n",
            "Epoch 551/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 551 || Epoch_D_Loss:0.1187 ||Epoch_G_Loss:0.1207 ||Epoch_E_Loss:0.1210\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 552/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 552 || Epoch_D_Loss:0.1117 ||Epoch_G_Loss:0.1124 ||Epoch_E_Loss:0.1309\n",
            "timer:  0.4247 sec.\n",
            "-------------\n",
            "Epoch 553/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 553 || Epoch_D_Loss:0.1163 ||Epoch_G_Loss:0.1233 ||Epoch_E_Loss:0.1233\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 554/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 554 || Epoch_D_Loss:0.1120 ||Epoch_G_Loss:0.1250 ||Epoch_E_Loss:0.1320\n",
            "timer:  0.4247 sec.\n",
            "-------------\n",
            "Epoch 555/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 555 || Epoch_D_Loss:0.1236 ||Epoch_G_Loss:0.1148 ||Epoch_E_Loss:0.1212\n",
            "timer:  0.4275 sec.\n",
            "-------------\n",
            "Epoch 556/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 556 || Epoch_D_Loss:0.1387 ||Epoch_G_Loss:0.1047 ||Epoch_E_Loss:0.1097\n",
            "timer:  0.4270 sec.\n",
            "-------------\n",
            "Epoch 557/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 557 || Epoch_D_Loss:0.1546 ||Epoch_G_Loss:0.0936 ||Epoch_E_Loss:0.0957\n",
            "timer:  0.4252 sec.\n",
            "-------------\n",
            "Epoch 558/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 558 || Epoch_D_Loss:0.1594 ||Epoch_G_Loss:0.0927 ||Epoch_E_Loss:0.1017\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 559/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 559 || Epoch_D_Loss:0.1476 ||Epoch_G_Loss:0.0917 ||Epoch_E_Loss:0.1066\n",
            "timer:  0.4255 sec.\n",
            "-------------\n",
            "Epoch 560/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 560 || Epoch_D_Loss:0.1379 ||Epoch_G_Loss:0.0997 ||Epoch_E_Loss:0.1094\n",
            "timer:  0.4321 sec.\n",
            "-------------\n",
            "Epoch 561/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 561 || Epoch_D_Loss:0.1301 ||Epoch_G_Loss:0.1054 ||Epoch_E_Loss:0.1139\n",
            "timer:  0.4501 sec.\n",
            "-------------\n",
            "Epoch 562/1500\n",
            "-------------\n",
            "（train）\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "-------------\n",
            "epoch 562 || Epoch_D_Loss:0.1311 ||Epoch_G_Loss:0.1011 ||Epoch_E_Loss:0.1134\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 563/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 563 || Epoch_D_Loss:0.1321 ||Epoch_G_Loss:0.0927 ||Epoch_E_Loss:0.1151\n",
            "timer:  0.4331 sec.\n",
            "-------------\n",
            "Epoch 564/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 564 || Epoch_D_Loss:0.1418 ||Epoch_G_Loss:0.1036 ||Epoch_E_Loss:0.1078\n",
            "timer:  0.4362 sec.\n",
            "-------------\n",
            "Epoch 565/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 565 || Epoch_D_Loss:0.1248 ||Epoch_G_Loss:0.1090 ||Epoch_E_Loss:0.1225\n",
            "timer:  0.4277 sec.\n",
            "-------------\n",
            "Epoch 566/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 566 || Epoch_D_Loss:0.1202 ||Epoch_G_Loss:0.1220 ||Epoch_E_Loss:0.1261\n",
            "timer:  0.4277 sec.\n",
            "-------------\n",
            "Epoch 567/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 567 || Epoch_D_Loss:0.1060 ||Epoch_G_Loss:0.1143 ||Epoch_E_Loss:0.1367\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 568/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 568 || Epoch_D_Loss:0.1202 ||Epoch_G_Loss:0.1079 ||Epoch_E_Loss:0.1263\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 569/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 569 || Epoch_D_Loss:0.1387 ||Epoch_G_Loss:0.1107 ||Epoch_E_Loss:0.1119\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 570/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 570 || Epoch_D_Loss:0.1416 ||Epoch_G_Loss:0.0881 ||Epoch_E_Loss:0.1126\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 571/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 571 || Epoch_D_Loss:0.1638 ||Epoch_G_Loss:0.0940 ||Epoch_E_Loss:0.0999\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 572/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 572 || Epoch_D_Loss:0.1550 ||Epoch_G_Loss:0.0958 ||Epoch_E_Loss:0.1052\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 573/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 573 || Epoch_D_Loss:0.1503 ||Epoch_G_Loss:0.0968 ||Epoch_E_Loss:0.1078\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 574/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 574 || Epoch_D_Loss:0.1452 ||Epoch_G_Loss:0.0981 ||Epoch_E_Loss:0.1156\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 575/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 575 || Epoch_D_Loss:0.1550 ||Epoch_G_Loss:0.0857 ||Epoch_E_Loss:0.1041\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 576/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 576 || Epoch_D_Loss:0.1532 ||Epoch_G_Loss:0.1024 ||Epoch_E_Loss:0.1003\n",
            "timer:  0.4273 sec.\n",
            "-------------\n",
            "Epoch 577/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 577 || Epoch_D_Loss:0.1505 ||Epoch_G_Loss:0.0936 ||Epoch_E_Loss:0.1208\n",
            "timer:  0.4280 sec.\n",
            "-------------\n",
            "Epoch 578/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 578 || Epoch_D_Loss:0.1508 ||Epoch_G_Loss:0.0992 ||Epoch_E_Loss:0.1089\n",
            "timer:  0.4271 sec.\n",
            "-------------\n",
            "Epoch 579/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 579 || Epoch_D_Loss:0.1482 ||Epoch_G_Loss:0.0910 ||Epoch_E_Loss:0.1135\n",
            "timer:  0.4284 sec.\n",
            "-------------\n",
            "Epoch 580/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 580 || Epoch_D_Loss:0.1460 ||Epoch_G_Loss:0.0964 ||Epoch_E_Loss:0.1112\n",
            "timer:  0.4281 sec.\n",
            "-------------\n",
            "Epoch 581/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 581 || Epoch_D_Loss:0.1363 ||Epoch_G_Loss:0.0995 ||Epoch_E_Loss:0.1162\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 582/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 582 || Epoch_D_Loss:0.1378 ||Epoch_G_Loss:0.1008 ||Epoch_E_Loss:0.1142\n",
            "timer:  0.4287 sec.\n",
            "-------------\n",
            "Epoch 583/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 583 || Epoch_D_Loss:0.1281 ||Epoch_G_Loss:0.1077 ||Epoch_E_Loss:0.1221\n",
            "timer:  0.4299 sec.\n",
            "-------------\n",
            "Epoch 584/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 584 || Epoch_D_Loss:0.1306 ||Epoch_G_Loss:0.1096 ||Epoch_E_Loss:0.1191\n",
            "timer:  0.4300 sec.\n",
            "-------------\n",
            "Epoch 585/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 585 || Epoch_D_Loss:0.1488 ||Epoch_G_Loss:0.0968 ||Epoch_E_Loss:0.1110\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 586/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 586 || Epoch_D_Loss:0.1620 ||Epoch_G_Loss:0.0916 ||Epoch_E_Loss:0.1054\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 587/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 587 || Epoch_D_Loss:0.1745 ||Epoch_G_Loss:0.0911 ||Epoch_E_Loss:0.0982\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 588/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 588 || Epoch_D_Loss:0.1549 ||Epoch_G_Loss:0.0914 ||Epoch_E_Loss:0.1128\n",
            "timer:  0.4276 sec.\n",
            "-------------\n",
            "Epoch 589/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 589 || Epoch_D_Loss:0.1332 ||Epoch_G_Loss:0.0980 ||Epoch_E_Loss:0.1259\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 590/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 590 || Epoch_D_Loss:0.1375 ||Epoch_G_Loss:0.1125 ||Epoch_E_Loss:0.1181\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 591/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 591 || Epoch_D_Loss:0.1381 ||Epoch_G_Loss:0.1051 ||Epoch_E_Loss:0.1252\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 592/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 592 || Epoch_D_Loss:0.1276 ||Epoch_G_Loss:0.1022 ||Epoch_E_Loss:0.1225\n",
            "timer:  0.4278 sec.\n",
            "-------------\n",
            "Epoch 593/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 593 || Epoch_D_Loss:0.1488 ||Epoch_G_Loss:0.0931 ||Epoch_E_Loss:0.1089\n",
            "timer:  0.4289 sec.\n",
            "-------------\n",
            "Epoch 594/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 594 || Epoch_D_Loss:0.1799 ||Epoch_G_Loss:0.0748 ||Epoch_E_Loss:0.0959\n",
            "timer:  0.4273 sec.\n",
            "-------------\n",
            "Epoch 595/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 595 || Epoch_D_Loss:0.1900 ||Epoch_G_Loss:0.0736 ||Epoch_E_Loss:0.0828\n",
            "timer:  0.4251 sec.\n",
            "-------------\n",
            "Epoch 596/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 596 || Epoch_D_Loss:0.1862 ||Epoch_G_Loss:0.0701 ||Epoch_E_Loss:0.0853\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 597/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 597 || Epoch_D_Loss:0.1609 ||Epoch_G_Loss:0.0867 ||Epoch_E_Loss:0.0938\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 598/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 598 || Epoch_D_Loss:0.1435 ||Epoch_G_Loss:0.0863 ||Epoch_E_Loss:0.1083\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 599/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 599 || Epoch_D_Loss:0.1395 ||Epoch_G_Loss:0.1076 ||Epoch_E_Loss:0.1038\n",
            "timer:  0.4278 sec.\n",
            "-------------\n",
            "Epoch 600/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 600 || Epoch_D_Loss:0.1325 ||Epoch_G_Loss:0.0896 ||Epoch_E_Loss:0.1094\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 601/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 601 || Epoch_D_Loss:0.1370 ||Epoch_G_Loss:0.0955 ||Epoch_E_Loss:0.1019\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 602/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 602 || Epoch_D_Loss:0.1364 ||Epoch_G_Loss:0.1030 ||Epoch_E_Loss:0.1039\n",
            "timer:  0.4290 sec.\n",
            "-------------\n",
            "Epoch 603/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 603 || Epoch_D_Loss:0.1399 ||Epoch_G_Loss:0.0951 ||Epoch_E_Loss:0.1053\n",
            "timer:  0.4240 sec.\n",
            "-------------\n",
            "Epoch 604/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 604 || Epoch_D_Loss:0.1460 ||Epoch_G_Loss:0.0920 ||Epoch_E_Loss:0.0968\n",
            "timer:  0.4250 sec.\n",
            "-------------\n",
            "Epoch 605/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 605 || Epoch_D_Loss:0.1486 ||Epoch_G_Loss:0.0851 ||Epoch_E_Loss:0.0977\n",
            "timer:  0.4258 sec.\n",
            "-------------\n",
            "Epoch 606/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 606 || Epoch_D_Loss:0.1538 ||Epoch_G_Loss:0.0945 ||Epoch_E_Loss:0.0922\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 607/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 607 || Epoch_D_Loss:0.1367 ||Epoch_G_Loss:0.0905 ||Epoch_E_Loss:0.1086\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 608/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 608 || Epoch_D_Loss:0.1242 ||Epoch_G_Loss:0.1114 ||Epoch_E_Loss:0.1133\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 609/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 609 || Epoch_D_Loss:0.1139 ||Epoch_G_Loss:0.1156 ||Epoch_E_Loss:0.1173\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 610/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 610 || Epoch_D_Loss:0.1190 ||Epoch_G_Loss:0.1105 ||Epoch_E_Loss:0.1172\n",
            "timer:  0.4281 sec.\n",
            "-------------\n",
            "Epoch 611/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 611 || Epoch_D_Loss:0.1238 ||Epoch_G_Loss:0.1001 ||Epoch_E_Loss:0.1109\n",
            "timer:  0.4310 sec.\n",
            "-------------\n",
            "Epoch 612/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 612 || Epoch_D_Loss:0.1376 ||Epoch_G_Loss:0.0967 ||Epoch_E_Loss:0.1038\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 613/1500\n",
            "-------------\n",
            "（train）\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "-------------\n",
            "epoch 613 || Epoch_D_Loss:0.1342 ||Epoch_G_Loss:0.0942 ||Epoch_E_Loss:0.1050\n",
            "timer:  0.4248 sec.\n",
            "-------------\n",
            "Epoch 614/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 614 || Epoch_D_Loss:0.1256 ||Epoch_G_Loss:0.1074 ||Epoch_E_Loss:0.1113\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 615/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 615 || Epoch_D_Loss:0.1186 ||Epoch_G_Loss:0.1081 ||Epoch_E_Loss:0.1161\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 616/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 616 || Epoch_D_Loss:0.1223 ||Epoch_G_Loss:0.1084 ||Epoch_E_Loss:0.1216\n",
            "timer:  0.4285 sec.\n",
            "-------------\n",
            "Epoch 617/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 617 || Epoch_D_Loss:0.1401 ||Epoch_G_Loss:0.0982 ||Epoch_E_Loss:0.1017\n",
            "timer:  0.4275 sec.\n",
            "-------------\n",
            "Epoch 618/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 618 || Epoch_D_Loss:0.1481 ||Epoch_G_Loss:0.0948 ||Epoch_E_Loss:0.0973\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 619/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 619 || Epoch_D_Loss:0.1411 ||Epoch_G_Loss:0.0935 ||Epoch_E_Loss:0.1066\n",
            "timer:  0.4271 sec.\n",
            "-------------\n",
            "Epoch 620/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 620 || Epoch_D_Loss:0.1311 ||Epoch_G_Loss:0.0999 ||Epoch_E_Loss:0.1125\n",
            "timer:  0.4287 sec.\n",
            "-------------\n",
            "Epoch 621/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 621 || Epoch_D_Loss:0.1244 ||Epoch_G_Loss:0.1036 ||Epoch_E_Loss:0.1157\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 622/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 622 || Epoch_D_Loss:0.1269 ||Epoch_G_Loss:0.1131 ||Epoch_E_Loss:0.1101\n",
            "timer:  0.4247 sec.\n",
            "-------------\n",
            "Epoch 623/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 623 || Epoch_D_Loss:0.1283 ||Epoch_G_Loss:0.0962 ||Epoch_E_Loss:0.1141\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 624/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 624 || Epoch_D_Loss:0.1429 ||Epoch_G_Loss:0.0893 ||Epoch_E_Loss:0.1054\n",
            "timer:  0.4281 sec.\n",
            "-------------\n",
            "Epoch 625/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 625 || Epoch_D_Loss:0.1478 ||Epoch_G_Loss:0.1000 ||Epoch_E_Loss:0.0987\n",
            "timer:  0.4258 sec.\n",
            "-------------\n",
            "Epoch 626/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 626 || Epoch_D_Loss:0.1433 ||Epoch_G_Loss:0.0983 ||Epoch_E_Loss:0.1039\n",
            "timer:  0.4255 sec.\n",
            "-------------\n",
            "Epoch 627/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 627 || Epoch_D_Loss:0.1304 ||Epoch_G_Loss:0.1014 ||Epoch_E_Loss:0.1068\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 628/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 628 || Epoch_D_Loss:0.1277 ||Epoch_G_Loss:0.0974 ||Epoch_E_Loss:0.1127\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 629/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 629 || Epoch_D_Loss:0.1244 ||Epoch_G_Loss:0.0997 ||Epoch_E_Loss:0.1132\n",
            "timer:  0.4250 sec.\n",
            "-------------\n",
            "Epoch 630/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 630 || Epoch_D_Loss:0.1213 ||Epoch_G_Loss:0.1205 ||Epoch_E_Loss:0.1107\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 631/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 631 || Epoch_D_Loss:0.1187 ||Epoch_G_Loss:0.1046 ||Epoch_E_Loss:0.1170\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 632/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 632 || Epoch_D_Loss:0.1294 ||Epoch_G_Loss:0.1012 ||Epoch_E_Loss:0.1040\n",
            "timer:  0.4278 sec.\n",
            "-------------\n",
            "Epoch 633/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 633 || Epoch_D_Loss:0.1389 ||Epoch_G_Loss:0.0899 ||Epoch_E_Loss:0.1007\n",
            "timer:  0.4253 sec.\n",
            "-------------\n",
            "Epoch 634/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 634 || Epoch_D_Loss:0.1451 ||Epoch_G_Loss:0.0887 ||Epoch_E_Loss:0.0948\n",
            "timer:  0.4244 sec.\n",
            "-------------\n",
            "Epoch 635/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 635 || Epoch_D_Loss:0.1407 ||Epoch_G_Loss:0.0884 ||Epoch_E_Loss:0.1089\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 636/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 636 || Epoch_D_Loss:0.1284 ||Epoch_G_Loss:0.1052 ||Epoch_E_Loss:0.1070\n",
            "timer:  0.4247 sec.\n",
            "-------------\n",
            "Epoch 637/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 637 || Epoch_D_Loss:0.1140 ||Epoch_G_Loss:0.1039 ||Epoch_E_Loss:0.1202\n",
            "timer:  0.4255 sec.\n",
            "-------------\n",
            "Epoch 638/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 638 || Epoch_D_Loss:0.1165 ||Epoch_G_Loss:0.1105 ||Epoch_E_Loss:0.1197\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 639/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 639 || Epoch_D_Loss:0.1334 ||Epoch_G_Loss:0.1018 ||Epoch_E_Loss:0.1049\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 640/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 640 || Epoch_D_Loss:0.1458 ||Epoch_G_Loss:0.0848 ||Epoch_E_Loss:0.1017\n",
            "timer:  0.4270 sec.\n",
            "-------------\n",
            "Epoch 641/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 641 || Epoch_D_Loss:0.1424 ||Epoch_G_Loss:0.0949 ||Epoch_E_Loss:0.1037\n",
            "timer:  0.4275 sec.\n",
            "-------------\n",
            "Epoch 642/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 642 || Epoch_D_Loss:0.1266 ||Epoch_G_Loss:0.1088 ||Epoch_E_Loss:0.1083\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 643/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 643 || Epoch_D_Loss:0.1158 ||Epoch_G_Loss:0.1120 ||Epoch_E_Loss:0.1180\n",
            "timer:  0.4255 sec.\n",
            "-------------\n",
            "Epoch 644/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 644 || Epoch_D_Loss:0.1312 ||Epoch_G_Loss:0.0986 ||Epoch_E_Loss:0.1138\n",
            "timer:  0.4253 sec.\n",
            "-------------\n",
            "Epoch 645/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 645 || Epoch_D_Loss:0.1358 ||Epoch_G_Loss:0.0924 ||Epoch_E_Loss:0.1026\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 646/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 646 || Epoch_D_Loss:0.1428 ||Epoch_G_Loss:0.0926 ||Epoch_E_Loss:0.0995\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 647/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 647 || Epoch_D_Loss:0.1450 ||Epoch_G_Loss:0.0920 ||Epoch_E_Loss:0.0968\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 648/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 648 || Epoch_D_Loss:0.1289 ||Epoch_G_Loss:0.0998 ||Epoch_E_Loss:0.1100\n",
            "timer:  0.4280 sec.\n",
            "-------------\n",
            "Epoch 649/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 649 || Epoch_D_Loss:0.1294 ||Epoch_G_Loss:0.1035 ||Epoch_E_Loss:0.1058\n",
            "timer:  0.4273 sec.\n",
            "-------------\n",
            "Epoch 650/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 650 || Epoch_D_Loss:0.1330 ||Epoch_G_Loss:0.0960 ||Epoch_E_Loss:0.1040\n",
            "timer:  0.4279 sec.\n",
            "-------------\n",
            "Epoch 651/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 651 || Epoch_D_Loss:0.1367 ||Epoch_G_Loss:0.0893 ||Epoch_E_Loss:0.1085\n",
            "timer:  0.4278 sec.\n",
            "-------------\n",
            "Epoch 652/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 652 || Epoch_D_Loss:0.1388 ||Epoch_G_Loss:0.0958 ||Epoch_E_Loss:0.1009\n",
            "timer:  0.4254 sec.\n",
            "-------------\n",
            "Epoch 653/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 653 || Epoch_D_Loss:0.1325 ||Epoch_G_Loss:0.0985 ||Epoch_E_Loss:0.1081\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 654/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 654 || Epoch_D_Loss:0.1264 ||Epoch_G_Loss:0.0979 ||Epoch_E_Loss:0.1119\n",
            "timer:  0.4279 sec.\n",
            "-------------\n",
            "Epoch 655/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 655 || Epoch_D_Loss:0.1271 ||Epoch_G_Loss:0.1005 ||Epoch_E_Loss:0.1058\n",
            "timer:  0.4306 sec.\n",
            "-------------\n",
            "Epoch 656/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 656 || Epoch_D_Loss:0.1280 ||Epoch_G_Loss:0.0953 ||Epoch_E_Loss:0.1077\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 657/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 657 || Epoch_D_Loss:0.1236 ||Epoch_G_Loss:0.0994 ||Epoch_E_Loss:0.1106\n",
            "timer:  0.4277 sec.\n",
            "-------------\n",
            "Epoch 658/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 658 || Epoch_D_Loss:0.1298 ||Epoch_G_Loss:0.0949 ||Epoch_E_Loss:0.1053\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 659/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 659 || Epoch_D_Loss:0.1360 ||Epoch_G_Loss:0.0901 ||Epoch_E_Loss:0.0980\n",
            "timer:  0.4279 sec.\n",
            "-------------\n",
            "Epoch 660/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 660 || Epoch_D_Loss:0.1378 ||Epoch_G_Loss:0.0855 ||Epoch_E_Loss:0.1041\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 661/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 661 || Epoch_D_Loss:0.1254 ||Epoch_G_Loss:0.1037 ||Epoch_E_Loss:0.1081\n",
            "timer:  0.4271 sec.\n",
            "-------------\n",
            "Epoch 662/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 662 || Epoch_D_Loss:0.1130 ||Epoch_G_Loss:0.1146 ||Epoch_E_Loss:0.1148\n",
            "timer:  0.4292 sec.\n",
            "-------------\n",
            "Epoch 663/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 663 || Epoch_D_Loss:0.1063 ||Epoch_G_Loss:0.1128 ||Epoch_E_Loss:0.1214\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 664/1500\n",
            "-------------\n",
            "（train）\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "-------------\n",
            "epoch 664 || Epoch_D_Loss:0.1175 ||Epoch_G_Loss:0.0996 ||Epoch_E_Loss:0.1190\n",
            "timer:  0.4253 sec.\n",
            "-------------\n",
            "Epoch 665/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 665 || Epoch_D_Loss:0.1243 ||Epoch_G_Loss:0.0988 ||Epoch_E_Loss:0.1101\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 666/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 666 || Epoch_D_Loss:0.1276 ||Epoch_G_Loss:0.0969 ||Epoch_E_Loss:0.1072\n",
            "timer:  0.4251 sec.\n",
            "-------------\n",
            "Epoch 667/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 667 || Epoch_D_Loss:0.1288 ||Epoch_G_Loss:0.1053 ||Epoch_E_Loss:0.1046\n",
            "timer:  0.4249 sec.\n",
            "-------------\n",
            "Epoch 668/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 668 || Epoch_D_Loss:0.1244 ||Epoch_G_Loss:0.1062 ||Epoch_E_Loss:0.1088\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 669/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 669 || Epoch_D_Loss:0.1253 ||Epoch_G_Loss:0.0978 ||Epoch_E_Loss:0.1116\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 670/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 670 || Epoch_D_Loss:0.1288 ||Epoch_G_Loss:0.1049 ||Epoch_E_Loss:0.1068\n",
            "timer:  0.4276 sec.\n",
            "-------------\n",
            "Epoch 671/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 671 || Epoch_D_Loss:0.1261 ||Epoch_G_Loss:0.0910 ||Epoch_E_Loss:0.1152\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 672/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 672 || Epoch_D_Loss:0.1289 ||Epoch_G_Loss:0.1109 ||Epoch_E_Loss:0.1058\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 673/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 673 || Epoch_D_Loss:0.1174 ||Epoch_G_Loss:0.1067 ||Epoch_E_Loss:0.1191\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 674/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 674 || Epoch_D_Loss:0.1104 ||Epoch_G_Loss:0.1152 ||Epoch_E_Loss:0.1173\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 675/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 675 || Epoch_D_Loss:0.1077 ||Epoch_G_Loss:0.1095 ||Epoch_E_Loss:0.1204\n",
            "timer:  0.4247 sec.\n",
            "-------------\n",
            "Epoch 676/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 676 || Epoch_D_Loss:0.1274 ||Epoch_G_Loss:0.0976 ||Epoch_E_Loss:0.1068\n",
            "timer:  0.4249 sec.\n",
            "-------------\n",
            "Epoch 677/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 677 || Epoch_D_Loss:0.1324 ||Epoch_G_Loss:0.0977 ||Epoch_E_Loss:0.1041\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 678/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 678 || Epoch_D_Loss:0.1297 ||Epoch_G_Loss:0.0955 ||Epoch_E_Loss:0.1074\n",
            "timer:  0.4276 sec.\n",
            "-------------\n",
            "Epoch 679/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 679 || Epoch_D_Loss:0.1247 ||Epoch_G_Loss:0.1048 ||Epoch_E_Loss:0.1099\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 680/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 680 || Epoch_D_Loss:0.1279 ||Epoch_G_Loss:0.1028 ||Epoch_E_Loss:0.1078\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 681/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 681 || Epoch_D_Loss:0.1345 ||Epoch_G_Loss:0.1011 ||Epoch_E_Loss:0.0992\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 682/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 682 || Epoch_D_Loss:0.1436 ||Epoch_G_Loss:0.0878 ||Epoch_E_Loss:0.1003\n",
            "timer:  0.4255 sec.\n",
            "-------------\n",
            "Epoch 683/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 683 || Epoch_D_Loss:0.1384 ||Epoch_G_Loss:0.0927 ||Epoch_E_Loss:0.1055\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 684/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 684 || Epoch_D_Loss:0.1136 ||Epoch_G_Loss:0.1098 ||Epoch_E_Loss:0.1213\n",
            "timer:  0.4244 sec.\n",
            "-------------\n",
            "Epoch 685/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 685 || Epoch_D_Loss:0.0995 ||Epoch_G_Loss:0.1251 ||Epoch_E_Loss:0.1345\n",
            "timer:  0.4254 sec.\n",
            "-------------\n",
            "Epoch 686/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 686 || Epoch_D_Loss:0.0992 ||Epoch_G_Loss:0.1250 ||Epoch_E_Loss:0.1298\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 687/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 687 || Epoch_D_Loss:0.1119 ||Epoch_G_Loss:0.1117 ||Epoch_E_Loss:0.1217\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 688/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 688 || Epoch_D_Loss:0.1293 ||Epoch_G_Loss:0.1012 ||Epoch_E_Loss:0.1071\n",
            "timer:  0.4277 sec.\n",
            "-------------\n",
            "Epoch 689/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 689 || Epoch_D_Loss:0.1505 ||Epoch_G_Loss:0.0847 ||Epoch_E_Loss:0.0989\n",
            "timer:  0.4270 sec.\n",
            "-------------\n",
            "Epoch 690/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 690 || Epoch_D_Loss:0.1551 ||Epoch_G_Loss:0.0945 ||Epoch_E_Loss:0.0877\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 691/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 691 || Epoch_D_Loss:0.1409 ||Epoch_G_Loss:0.0875 ||Epoch_E_Loss:0.1010\n",
            "timer:  0.4242 sec.\n",
            "-------------\n",
            "Epoch 692/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 692 || Epoch_D_Loss:0.1242 ||Epoch_G_Loss:0.1011 ||Epoch_E_Loss:0.1073\n",
            "timer:  0.4247 sec.\n",
            "-------------\n",
            "Epoch 693/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 693 || Epoch_D_Loss:0.1161 ||Epoch_G_Loss:0.1039 ||Epoch_E_Loss:0.1151\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 694/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 694 || Epoch_D_Loss:0.1160 ||Epoch_G_Loss:0.1119 ||Epoch_E_Loss:0.1157\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 695/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 695 || Epoch_D_Loss:0.1104 ||Epoch_G_Loss:0.1138 ||Epoch_E_Loss:0.1189\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 696/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 696 || Epoch_D_Loss:0.1095 ||Epoch_G_Loss:0.1149 ||Epoch_E_Loss:0.1239\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 697/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 697 || Epoch_D_Loss:0.1193 ||Epoch_G_Loss:0.1090 ||Epoch_E_Loss:0.1148\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 698/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 698 || Epoch_D_Loss:0.1329 ||Epoch_G_Loss:0.0922 ||Epoch_E_Loss:0.1070\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 699/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 699 || Epoch_D_Loss:0.1494 ||Epoch_G_Loss:0.0873 ||Epoch_E_Loss:0.0939\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 700/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 700 || Epoch_D_Loss:0.1369 ||Epoch_G_Loss:0.0886 ||Epoch_E_Loss:0.1056\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 701/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 701 || Epoch_D_Loss:0.1255 ||Epoch_G_Loss:0.1076 ||Epoch_E_Loss:0.1091\n",
            "timer:  0.4258 sec.\n",
            "-------------\n",
            "Epoch 702/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 702 || Epoch_D_Loss:0.1166 ||Epoch_G_Loss:0.1070 ||Epoch_E_Loss:0.1245\n",
            "timer:  0.4248 sec.\n",
            "-------------\n",
            "Epoch 703/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 703 || Epoch_D_Loss:0.1189 ||Epoch_G_Loss:0.1137 ||Epoch_E_Loss:0.1183\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 704/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 704 || Epoch_D_Loss:0.1194 ||Epoch_G_Loss:0.1039 ||Epoch_E_Loss:0.1231\n",
            "timer:  0.4276 sec.\n",
            "-------------\n",
            "Epoch 705/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 705 || Epoch_D_Loss:0.1358 ||Epoch_G_Loss:0.1025 ||Epoch_E_Loss:0.1069\n",
            "timer:  0.4244 sec.\n",
            "-------------\n",
            "Epoch 706/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 706 || Epoch_D_Loss:0.1386 ||Epoch_G_Loss:0.1068 ||Epoch_E_Loss:0.1035\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 707/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 707 || Epoch_D_Loss:0.1310 ||Epoch_G_Loss:0.1075 ||Epoch_E_Loss:0.1137\n",
            "timer:  0.4273 sec.\n",
            "-------------\n",
            "Epoch 708/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 708 || Epoch_D_Loss:0.1123 ||Epoch_G_Loss:0.1125 ||Epoch_E_Loss:0.1273\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 709/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 709 || Epoch_D_Loss:0.1044 ||Epoch_G_Loss:0.1178 ||Epoch_E_Loss:0.1304\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 710/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 710 || Epoch_D_Loss:0.1145 ||Epoch_G_Loss:0.1110 ||Epoch_E_Loss:0.1208\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 711/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 711 || Epoch_D_Loss:0.1264 ||Epoch_G_Loss:0.1152 ||Epoch_E_Loss:0.1151\n",
            "timer:  0.4247 sec.\n",
            "-------------\n",
            "Epoch 712/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 712 || Epoch_D_Loss:0.1347 ||Epoch_G_Loss:0.0994 ||Epoch_E_Loss:0.1074\n",
            "timer:  0.4251 sec.\n",
            "-------------\n",
            "Epoch 713/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 713 || Epoch_D_Loss:0.1385 ||Epoch_G_Loss:0.0992 ||Epoch_E_Loss:0.1077\n",
            "timer:  0.4253 sec.\n",
            "-------------\n",
            "Epoch 714/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 714 || Epoch_D_Loss:0.1323 ||Epoch_G_Loss:0.1056 ||Epoch_E_Loss:0.1154\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 715/1500\n",
            "-------------\n",
            "（train）\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "-------------\n",
            "epoch 715 || Epoch_D_Loss:0.1224 ||Epoch_G_Loss:0.1130 ||Epoch_E_Loss:0.1203\n",
            "timer:  0.4289 sec.\n",
            "-------------\n",
            "Epoch 716/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 716 || Epoch_D_Loss:0.1221 ||Epoch_G_Loss:0.1038 ||Epoch_E_Loss:0.1221\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 717/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 717 || Epoch_D_Loss:0.1264 ||Epoch_G_Loss:0.1227 ||Epoch_E_Loss:0.1132\n",
            "timer:  0.4281 sec.\n",
            "-------------\n",
            "Epoch 718/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 718 || Epoch_D_Loss:0.1106 ||Epoch_G_Loss:0.1123 ||Epoch_E_Loss:0.1315\n",
            "timer:  0.4258 sec.\n",
            "-------------\n",
            "Epoch 719/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 719 || Epoch_D_Loss:0.1199 ||Epoch_G_Loss:0.1023 ||Epoch_E_Loss:0.1255\n",
            "timer:  0.4277 sec.\n",
            "-------------\n",
            "Epoch 720/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 720 || Epoch_D_Loss:0.1326 ||Epoch_G_Loss:0.1101 ||Epoch_E_Loss:0.1145\n",
            "timer:  0.4258 sec.\n",
            "-------------\n",
            "Epoch 721/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 721 || Epoch_D_Loss:0.1402 ||Epoch_G_Loss:0.1128 ||Epoch_E_Loss:0.1053\n",
            "timer:  0.4253 sec.\n",
            "-------------\n",
            "Epoch 722/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 722 || Epoch_D_Loss:0.1356 ||Epoch_G_Loss:0.1045 ||Epoch_E_Loss:0.1171\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 723/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 723 || Epoch_D_Loss:0.1291 ||Epoch_G_Loss:0.1073 ||Epoch_E_Loss:0.1189\n",
            "timer:  0.4271 sec.\n",
            "-------------\n",
            "Epoch 724/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 724 || Epoch_D_Loss:0.1224 ||Epoch_G_Loss:0.1016 ||Epoch_E_Loss:0.1209\n",
            "timer:  0.4307 sec.\n",
            "-------------\n",
            "Epoch 725/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 725 || Epoch_D_Loss:0.1218 ||Epoch_G_Loss:0.1047 ||Epoch_E_Loss:0.1215\n",
            "timer:  0.4296 sec.\n",
            "-------------\n",
            "Epoch 726/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 726 || Epoch_D_Loss:0.1266 ||Epoch_G_Loss:0.1051 ||Epoch_E_Loss:0.1223\n",
            "timer:  0.4278 sec.\n",
            "-------------\n",
            "Epoch 727/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 727 || Epoch_D_Loss:0.1308 ||Epoch_G_Loss:0.1117 ||Epoch_E_Loss:0.1120\n",
            "timer:  0.4276 sec.\n",
            "-------------\n",
            "Epoch 728/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 728 || Epoch_D_Loss:0.1303 ||Epoch_G_Loss:0.1042 ||Epoch_E_Loss:0.1238\n",
            "timer:  0.4273 sec.\n",
            "-------------\n",
            "Epoch 729/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 729 || Epoch_D_Loss:0.1257 ||Epoch_G_Loss:0.1224 ||Epoch_E_Loss:0.1161\n",
            "timer:  0.4251 sec.\n",
            "-------------\n",
            "Epoch 730/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 730 || Epoch_D_Loss:0.1221 ||Epoch_G_Loss:0.1047 ||Epoch_E_Loss:0.1328\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 731/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 731 || Epoch_D_Loss:0.1368 ||Epoch_G_Loss:0.1068 ||Epoch_E_Loss:0.1134\n",
            "timer:  0.4276 sec.\n",
            "-------------\n",
            "Epoch 732/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 732 || Epoch_D_Loss:0.1295 ||Epoch_G_Loss:0.1049 ||Epoch_E_Loss:0.1235\n",
            "timer:  0.4273 sec.\n",
            "-------------\n",
            "Epoch 733/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 733 || Epoch_D_Loss:0.1308 ||Epoch_G_Loss:0.1117 ||Epoch_E_Loss:0.1151\n",
            "timer:  0.4294 sec.\n",
            "-------------\n",
            "Epoch 734/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 734 || Epoch_D_Loss:0.1267 ||Epoch_G_Loss:0.1077 ||Epoch_E_Loss:0.1220\n",
            "timer:  0.4281 sec.\n",
            "-------------\n",
            "Epoch 735/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 735 || Epoch_D_Loss:0.1295 ||Epoch_G_Loss:0.1260 ||Epoch_E_Loss:0.1126\n",
            "timer:  0.4271 sec.\n",
            "-------------\n",
            "Epoch 736/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 736 || Epoch_D_Loss:0.1216 ||Epoch_G_Loss:0.1165 ||Epoch_E_Loss:0.1325\n",
            "timer:  0.4241 sec.\n",
            "-------------\n",
            "Epoch 737/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 737 || Epoch_D_Loss:0.1285 ||Epoch_G_Loss:0.1088 ||Epoch_E_Loss:0.1187\n",
            "timer:  0.4241 sec.\n",
            "-------------\n",
            "Epoch 738/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 738 || Epoch_D_Loss:0.1300 ||Epoch_G_Loss:0.1033 ||Epoch_E_Loss:0.1198\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 739/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 739 || Epoch_D_Loss:0.1359 ||Epoch_G_Loss:0.1064 ||Epoch_E_Loss:0.1133\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 740/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 740 || Epoch_D_Loss:0.1404 ||Epoch_G_Loss:0.1016 ||Epoch_E_Loss:0.1177\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 741/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 741 || Epoch_D_Loss:0.1290 ||Epoch_G_Loss:0.1109 ||Epoch_E_Loss:0.1192\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 742/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 742 || Epoch_D_Loss:0.1297 ||Epoch_G_Loss:0.1123 ||Epoch_E_Loss:0.1158\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 743/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 743 || Epoch_D_Loss:0.1287 ||Epoch_G_Loss:0.1103 ||Epoch_E_Loss:0.1234\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 744/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 744 || Epoch_D_Loss:0.1208 ||Epoch_G_Loss:0.1085 ||Epoch_E_Loss:0.1320\n",
            "timer:  0.4252 sec.\n",
            "-------------\n",
            "Epoch 745/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 745 || Epoch_D_Loss:0.1188 ||Epoch_G_Loss:0.1094 ||Epoch_E_Loss:0.1251\n",
            "timer:  0.4239 sec.\n",
            "-------------\n",
            "Epoch 746/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 746 || Epoch_D_Loss:0.1243 ||Epoch_G_Loss:0.0996 ||Epoch_E_Loss:0.1221\n",
            "timer:  0.4258 sec.\n",
            "-------------\n",
            "Epoch 747/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 747 || Epoch_D_Loss:0.1360 ||Epoch_G_Loss:0.1125 ||Epoch_E_Loss:0.1056\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 748/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 748 || Epoch_D_Loss:0.1388 ||Epoch_G_Loss:0.0936 ||Epoch_E_Loss:0.1226\n",
            "timer:  0.4273 sec.\n",
            "-------------\n",
            "Epoch 749/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 749 || Epoch_D_Loss:0.1324 ||Epoch_G_Loss:0.0998 ||Epoch_E_Loss:0.1178\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 750/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 750 || Epoch_D_Loss:0.1361 ||Epoch_G_Loss:0.1092 ||Epoch_E_Loss:0.1132\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 751/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 751 || Epoch_D_Loss:0.1266 ||Epoch_G_Loss:0.1145 ||Epoch_E_Loss:0.1224\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 752/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 752 || Epoch_D_Loss:0.1199 ||Epoch_G_Loss:0.1172 ||Epoch_E_Loss:0.1264\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 753/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 753 || Epoch_D_Loss:0.1200 ||Epoch_G_Loss:0.1161 ||Epoch_E_Loss:0.1245\n",
            "timer:  0.4280 sec.\n",
            "-------------\n",
            "Epoch 754/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 754 || Epoch_D_Loss:0.1318 ||Epoch_G_Loss:0.1136 ||Epoch_E_Loss:0.1210\n",
            "timer:  0.4289 sec.\n",
            "-------------\n",
            "Epoch 755/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 755 || Epoch_D_Loss:0.1347 ||Epoch_G_Loss:0.1029 ||Epoch_E_Loss:0.1166\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 756/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 756 || Epoch_D_Loss:0.1312 ||Epoch_G_Loss:0.1096 ||Epoch_E_Loss:0.1182\n",
            "timer:  0.4251 sec.\n",
            "-------------\n",
            "Epoch 757/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 757 || Epoch_D_Loss:0.1275 ||Epoch_G_Loss:0.1107 ||Epoch_E_Loss:0.1158\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 758/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 758 || Epoch_D_Loss:0.1261 ||Epoch_G_Loss:0.1068 ||Epoch_E_Loss:0.1183\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 759/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 759 || Epoch_D_Loss:0.1292 ||Epoch_G_Loss:0.1122 ||Epoch_E_Loss:0.1133\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 760/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 760 || Epoch_D_Loss:0.1332 ||Epoch_G_Loss:0.1090 ||Epoch_E_Loss:0.1227\n",
            "timer:  0.4273 sec.\n",
            "-------------\n",
            "Epoch 761/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 761 || Epoch_D_Loss:0.1232 ||Epoch_G_Loss:0.1206 ||Epoch_E_Loss:0.1226\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 762/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 762 || Epoch_D_Loss:0.1226 ||Epoch_G_Loss:0.1115 ||Epoch_E_Loss:0.1207\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 763/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 763 || Epoch_D_Loss:0.1244 ||Epoch_G_Loss:0.0990 ||Epoch_E_Loss:0.1212\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 764/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 764 || Epoch_D_Loss:0.1369 ||Epoch_G_Loss:0.1074 ||Epoch_E_Loss:0.1147\n",
            "timer:  0.4252 sec.\n",
            "-------------\n",
            "Epoch 765/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 765 || Epoch_D_Loss:0.1191 ||Epoch_G_Loss:0.0983 ||Epoch_E_Loss:0.1224\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 766/1500\n",
            "-------------\n",
            "（train）\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "-------------\n",
            "epoch 766 || Epoch_D_Loss:0.1131 ||Epoch_G_Loss:0.1217 ||Epoch_E_Loss:0.1227\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 767/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 767 || Epoch_D_Loss:0.1173 ||Epoch_G_Loss:0.1116 ||Epoch_E_Loss:0.1309\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 768/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 768 || Epoch_D_Loss:0.1174 ||Epoch_G_Loss:0.1108 ||Epoch_E_Loss:0.1238\n",
            "timer:  0.4242 sec.\n",
            "-------------\n",
            "Epoch 769/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 769 || Epoch_D_Loss:0.1287 ||Epoch_G_Loss:0.1029 ||Epoch_E_Loss:0.1151\n",
            "timer:  0.4240 sec.\n",
            "-------------\n",
            "Epoch 770/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 770 || Epoch_D_Loss:0.1331 ||Epoch_G_Loss:0.1042 ||Epoch_E_Loss:0.1133\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 771/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 771 || Epoch_D_Loss:0.1375 ||Epoch_G_Loss:0.1010 ||Epoch_E_Loss:0.1053\n",
            "timer:  0.4244 sec.\n",
            "-------------\n",
            "Epoch 772/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 772 || Epoch_D_Loss:0.1382 ||Epoch_G_Loss:0.1003 ||Epoch_E_Loss:0.1109\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 773/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 773 || Epoch_D_Loss:0.1316 ||Epoch_G_Loss:0.1028 ||Epoch_E_Loss:0.1142\n",
            "timer:  0.4280 sec.\n",
            "-------------\n",
            "Epoch 774/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 774 || Epoch_D_Loss:0.1163 ||Epoch_G_Loss:0.1152 ||Epoch_E_Loss:0.1198\n",
            "timer:  0.4242 sec.\n",
            "-------------\n",
            "Epoch 775/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 775 || Epoch_D_Loss:0.1212 ||Epoch_G_Loss:0.1126 ||Epoch_E_Loss:0.1206\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 776/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 776 || Epoch_D_Loss:0.1180 ||Epoch_G_Loss:0.1067 ||Epoch_E_Loss:0.1219\n",
            "timer:  0.4239 sec.\n",
            "-------------\n",
            "Epoch 777/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 777 || Epoch_D_Loss:0.1233 ||Epoch_G_Loss:0.1038 ||Epoch_E_Loss:0.1165\n",
            "timer:  0.4247 sec.\n",
            "-------------\n",
            "Epoch 778/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 778 || Epoch_D_Loss:0.1339 ||Epoch_G_Loss:0.1062 ||Epoch_E_Loss:0.1072\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 779/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 779 || Epoch_D_Loss:0.1325 ||Epoch_G_Loss:0.1011 ||Epoch_E_Loss:0.1096\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 780/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 780 || Epoch_D_Loss:0.1366 ||Epoch_G_Loss:0.1055 ||Epoch_E_Loss:0.1026\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 781/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 781 || Epoch_D_Loss:0.1413 ||Epoch_G_Loss:0.1021 ||Epoch_E_Loss:0.1013\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 782/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 782 || Epoch_D_Loss:0.1434 ||Epoch_G_Loss:0.0981 ||Epoch_E_Loss:0.1012\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 783/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 783 || Epoch_D_Loss:0.1438 ||Epoch_G_Loss:0.0978 ||Epoch_E_Loss:0.1047\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 784/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 784 || Epoch_D_Loss:0.1265 ||Epoch_G_Loss:0.1054 ||Epoch_E_Loss:0.1077\n",
            "timer:  0.4270 sec.\n",
            "-------------\n",
            "Epoch 785/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 785 || Epoch_D_Loss:0.1221 ||Epoch_G_Loss:0.1071 ||Epoch_E_Loss:0.1196\n",
            "timer:  0.4255 sec.\n",
            "-------------\n",
            "Epoch 786/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 786 || Epoch_D_Loss:0.1097 ||Epoch_G_Loss:0.1247 ||Epoch_E_Loss:0.1232\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 787/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 787 || Epoch_D_Loss:0.1039 ||Epoch_G_Loss:0.1197 ||Epoch_E_Loss:0.1317\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 788/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 788 || Epoch_D_Loss:0.1089 ||Epoch_G_Loss:0.1132 ||Epoch_E_Loss:0.1259\n",
            "timer:  0.4251 sec.\n",
            "-------------\n",
            "Epoch 789/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 789 || Epoch_D_Loss:0.1375 ||Epoch_G_Loss:0.0958 ||Epoch_E_Loss:0.1055\n",
            "timer:  0.4246 sec.\n",
            "-------------\n",
            "Epoch 790/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 790 || Epoch_D_Loss:0.1615 ||Epoch_G_Loss:0.0861 ||Epoch_E_Loss:0.0879\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 791/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 791 || Epoch_D_Loss:0.1561 ||Epoch_G_Loss:0.0852 ||Epoch_E_Loss:0.0947\n",
            "timer:  0.4270 sec.\n",
            "-------------\n",
            "Epoch 792/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 792 || Epoch_D_Loss:0.1282 ||Epoch_G_Loss:0.0960 ||Epoch_E_Loss:0.1119\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 793/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 793 || Epoch_D_Loss:0.1106 ||Epoch_G_Loss:0.1148 ||Epoch_E_Loss:0.1188\n",
            "timer:  0.4251 sec.\n",
            "-------------\n",
            "Epoch 794/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 794 || Epoch_D_Loss:0.1024 ||Epoch_G_Loss:0.1191 ||Epoch_E_Loss:0.1250\n",
            "timer:  0.4281 sec.\n",
            "-------------\n",
            "Epoch 795/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 795 || Epoch_D_Loss:0.1046 ||Epoch_G_Loss:0.1262 ||Epoch_E_Loss:0.1166\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 796/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 796 || Epoch_D_Loss:0.1181 ||Epoch_G_Loss:0.1086 ||Epoch_E_Loss:0.1249\n",
            "timer:  0.4270 sec.\n",
            "-------------\n",
            "Epoch 797/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 797 || Epoch_D_Loss:0.1255 ||Epoch_G_Loss:0.1094 ||Epoch_E_Loss:0.1050\n",
            "timer:  0.4275 sec.\n",
            "-------------\n",
            "Epoch 798/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 798 || Epoch_D_Loss:0.1275 ||Epoch_G_Loss:0.0967 ||Epoch_E_Loss:0.1107\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 799/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 799 || Epoch_D_Loss:0.1304 ||Epoch_G_Loss:0.1003 ||Epoch_E_Loss:0.1043\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 800/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 800 || Epoch_D_Loss:0.1250 ||Epoch_G_Loss:0.1006 ||Epoch_E_Loss:0.1108\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 801/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 801 || Epoch_D_Loss:0.1148 ||Epoch_G_Loss:0.1140 ||Epoch_E_Loss:0.1169\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 802/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 802 || Epoch_D_Loss:0.1038 ||Epoch_G_Loss:0.1126 ||Epoch_E_Loss:0.1316\n",
            "timer:  0.4252 sec.\n",
            "-------------\n",
            "Epoch 803/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 803 || Epoch_D_Loss:0.1064 ||Epoch_G_Loss:0.1219 ||Epoch_E_Loss:0.1241\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 804/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 804 || Epoch_D_Loss:0.1148 ||Epoch_G_Loss:0.1031 ||Epoch_E_Loss:0.1246\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 805/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 805 || Epoch_D_Loss:0.1179 ||Epoch_G_Loss:0.1057 ||Epoch_E_Loss:0.1158\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 806/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 806 || Epoch_D_Loss:0.1206 ||Epoch_G_Loss:0.0990 ||Epoch_E_Loss:0.1148\n",
            "timer:  0.4294 sec.\n",
            "-------------\n",
            "Epoch 807/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 807 || Epoch_D_Loss:0.1254 ||Epoch_G_Loss:0.1031 ||Epoch_E_Loss:0.1094\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 808/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 808 || Epoch_D_Loss:0.1241 ||Epoch_G_Loss:0.1080 ||Epoch_E_Loss:0.1191\n",
            "timer:  0.4284 sec.\n",
            "-------------\n",
            "Epoch 809/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 809 || Epoch_D_Loss:0.1092 ||Epoch_G_Loss:0.1201 ||Epoch_E_Loss:0.1183\n",
            "timer:  0.4287 sec.\n",
            "-------------\n",
            "Epoch 810/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 810 || Epoch_D_Loss:0.0998 ||Epoch_G_Loss:0.1229 ||Epoch_E_Loss:0.1321\n",
            "timer:  0.4250 sec.\n",
            "-------------\n",
            "Epoch 811/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 811 || Epoch_D_Loss:0.1002 ||Epoch_G_Loss:0.1266 ||Epoch_E_Loss:0.1301\n",
            "timer:  0.4246 sec.\n",
            "-------------\n",
            "Epoch 812/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 812 || Epoch_D_Loss:0.1056 ||Epoch_G_Loss:0.1187 ||Epoch_E_Loss:0.1303\n",
            "timer:  0.4254 sec.\n",
            "-------------\n",
            "Epoch 813/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 813 || Epoch_D_Loss:0.1141 ||Epoch_G_Loss:0.1212 ||Epoch_E_Loss:0.1161\n",
            "timer:  0.4258 sec.\n",
            "-------------\n",
            "Epoch 814/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 814 || Epoch_D_Loss:0.1267 ||Epoch_G_Loss:0.0979 ||Epoch_E_Loss:0.1140\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 815/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 815 || Epoch_D_Loss:0.1458 ||Epoch_G_Loss:0.1009 ||Epoch_E_Loss:0.1035\n",
            "timer:  0.4280 sec.\n",
            "-------------\n",
            "Epoch 816/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 816 || Epoch_D_Loss:0.1402 ||Epoch_G_Loss:0.0962 ||Epoch_E_Loss:0.1087\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 817/1500\n",
            "-------------\n",
            "（train）\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "-------------\n",
            "epoch 817 || Epoch_D_Loss:0.1215 ||Epoch_G_Loss:0.1098 ||Epoch_E_Loss:0.1123\n",
            "timer:  0.4244 sec.\n",
            "-------------\n",
            "Epoch 818/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 818 || Epoch_D_Loss:0.1047 ||Epoch_G_Loss:0.1304 ||Epoch_E_Loss:0.1283\n",
            "timer:  0.4240 sec.\n",
            "-------------\n",
            "Epoch 819/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 819 || Epoch_D_Loss:0.0995 ||Epoch_G_Loss:0.1246 ||Epoch_E_Loss:0.1331\n",
            "timer:  0.4245 sec.\n",
            "-------------\n",
            "Epoch 820/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 820 || Epoch_D_Loss:0.1005 ||Epoch_G_Loss:0.1172 ||Epoch_E_Loss:0.1379\n",
            "timer:  0.4251 sec.\n",
            "-------------\n",
            "Epoch 821/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 821 || Epoch_D_Loss:0.1075 ||Epoch_G_Loss:0.1209 ||Epoch_E_Loss:0.1244\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 822/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 822 || Epoch_D_Loss:0.1117 ||Epoch_G_Loss:0.1212 ||Epoch_E_Loss:0.1220\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 823/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 823 || Epoch_D_Loss:0.1214 ||Epoch_G_Loss:0.1129 ||Epoch_E_Loss:0.1155\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 824/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 824 || Epoch_D_Loss:0.1334 ||Epoch_G_Loss:0.0972 ||Epoch_E_Loss:0.1052\n",
            "timer:  0.4248 sec.\n",
            "-------------\n",
            "Epoch 825/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 825 || Epoch_D_Loss:0.1505 ||Epoch_G_Loss:0.0897 ||Epoch_E_Loss:0.0997\n",
            "timer:  0.4250 sec.\n",
            "-------------\n",
            "Epoch 826/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 826 || Epoch_D_Loss:0.1416 ||Epoch_G_Loss:0.0959 ||Epoch_E_Loss:0.0985\n",
            "timer:  0.4258 sec.\n",
            "-------------\n",
            "Epoch 827/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 827 || Epoch_D_Loss:0.1383 ||Epoch_G_Loss:0.0903 ||Epoch_E_Loss:0.1147\n",
            "timer:  0.4243 sec.\n",
            "-------------\n",
            "Epoch 828/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 828 || Epoch_D_Loss:0.1098 ||Epoch_G_Loss:0.1133 ||Epoch_E_Loss:0.1300\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 829/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 829 || Epoch_D_Loss:0.0980 ||Epoch_G_Loss:0.1369 ||Epoch_E_Loss:0.1345\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 830/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 830 || Epoch_D_Loss:0.0937 ||Epoch_G_Loss:0.1287 ||Epoch_E_Loss:0.1373\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 831/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 831 || Epoch_D_Loss:0.1115 ||Epoch_G_Loss:0.1286 ||Epoch_E_Loss:0.1223\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 832/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 832 || Epoch_D_Loss:0.1221 ||Epoch_G_Loss:0.1107 ||Epoch_E_Loss:0.1168\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 833/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 833 || Epoch_D_Loss:0.1288 ||Epoch_G_Loss:0.1027 ||Epoch_E_Loss:0.1121\n",
            "timer:  0.4253 sec.\n",
            "-------------\n",
            "Epoch 834/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 834 || Epoch_D_Loss:0.1257 ||Epoch_G_Loss:0.1099 ||Epoch_E_Loss:0.1137\n",
            "timer:  0.4245 sec.\n",
            "-------------\n",
            "Epoch 835/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 835 || Epoch_D_Loss:0.1213 ||Epoch_G_Loss:0.1124 ||Epoch_E_Loss:0.1180\n",
            "timer:  0.4244 sec.\n",
            "-------------\n",
            "Epoch 836/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 836 || Epoch_D_Loss:0.1129 ||Epoch_G_Loss:0.1071 ||Epoch_E_Loss:0.1249\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 837/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 837 || Epoch_D_Loss:0.1167 ||Epoch_G_Loss:0.1150 ||Epoch_E_Loss:0.1218\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 838/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 838 || Epoch_D_Loss:0.1122 ||Epoch_G_Loss:0.1177 ||Epoch_E_Loss:0.1255\n",
            "timer:  0.4254 sec.\n",
            "-------------\n",
            "Epoch 839/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 839 || Epoch_D_Loss:0.1084 ||Epoch_G_Loss:0.1203 ||Epoch_E_Loss:0.1229\n",
            "timer:  0.4275 sec.\n",
            "-------------\n",
            "Epoch 840/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 840 || Epoch_D_Loss:0.1139 ||Epoch_G_Loss:0.1109 ||Epoch_E_Loss:0.1241\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 841/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 841 || Epoch_D_Loss:0.1297 ||Epoch_G_Loss:0.1026 ||Epoch_E_Loss:0.1156\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 842/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 842 || Epoch_D_Loss:0.1342 ||Epoch_G_Loss:0.1029 ||Epoch_E_Loss:0.1081\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 843/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 843 || Epoch_D_Loss:0.1269 ||Epoch_G_Loss:0.1096 ||Epoch_E_Loss:0.1123\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 844/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 844 || Epoch_D_Loss:0.1109 ||Epoch_G_Loss:0.1128 ||Epoch_E_Loss:0.1297\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 845/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 845 || Epoch_D_Loss:0.1016 ||Epoch_G_Loss:0.1242 ||Epoch_E_Loss:0.1308\n",
            "timer:  0.4254 sec.\n",
            "-------------\n",
            "Epoch 846/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 846 || Epoch_D_Loss:0.1039 ||Epoch_G_Loss:0.1248 ||Epoch_E_Loss:0.1334\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 847/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 847 || Epoch_D_Loss:0.1215 ||Epoch_G_Loss:0.1171 ||Epoch_E_Loss:0.1178\n",
            "timer:  0.4282 sec.\n",
            "-------------\n",
            "Epoch 848/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 848 || Epoch_D_Loss:0.1365 ||Epoch_G_Loss:0.1111 ||Epoch_E_Loss:0.1104\n",
            "timer:  0.4246 sec.\n",
            "-------------\n",
            "Epoch 849/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 849 || Epoch_D_Loss:0.1384 ||Epoch_G_Loss:0.0906 ||Epoch_E_Loss:0.1197\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 850/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 850 || Epoch_D_Loss:0.1383 ||Epoch_G_Loss:0.1099 ||Epoch_E_Loss:0.1082\n",
            "timer:  0.4278 sec.\n",
            "-------------\n",
            "Epoch 851/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 851 || Epoch_D_Loss:0.1148 ||Epoch_G_Loss:0.1124 ||Epoch_E_Loss:0.1252\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 852/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 852 || Epoch_D_Loss:0.1042 ||Epoch_G_Loss:0.1342 ||Epoch_E_Loss:0.1281\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 853/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 853 || Epoch_D_Loss:0.0995 ||Epoch_G_Loss:0.1294 ||Epoch_E_Loss:0.1426\n",
            "timer:  0.4253 sec.\n",
            "-------------\n",
            "Epoch 854/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 854 || Epoch_D_Loss:0.1130 ||Epoch_G_Loss:0.1195 ||Epoch_E_Loss:0.1288\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 855/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 855 || Epoch_D_Loss:0.1231 ||Epoch_G_Loss:0.1134 ||Epoch_E_Loss:0.1186\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 856/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 856 || Epoch_D_Loss:0.1290 ||Epoch_G_Loss:0.1028 ||Epoch_E_Loss:0.1096\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 857/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 857 || Epoch_D_Loss:0.1322 ||Epoch_G_Loss:0.1055 ||Epoch_E_Loss:0.1148\n",
            "timer:  0.4286 sec.\n",
            "-------------\n",
            "Epoch 858/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 858 || Epoch_D_Loss:0.1126 ||Epoch_G_Loss:0.1050 ||Epoch_E_Loss:0.1261\n",
            "timer:  0.4300 sec.\n",
            "-------------\n",
            "Epoch 859/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 859 || Epoch_D_Loss:0.1167 ||Epoch_G_Loss:0.1281 ||Epoch_E_Loss:0.1170\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 860/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 860 || Epoch_D_Loss:0.1211 ||Epoch_G_Loss:0.1115 ||Epoch_E_Loss:0.1230\n",
            "timer:  0.4253 sec.\n",
            "-------------\n",
            "Epoch 861/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 861 || Epoch_D_Loss:0.1229 ||Epoch_G_Loss:0.1120 ||Epoch_E_Loss:0.1223\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 862/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 862 || Epoch_D_Loss:0.1239 ||Epoch_G_Loss:0.1175 ||Epoch_E_Loss:0.1186\n",
            "timer:  0.4253 sec.\n",
            "-------------\n",
            "Epoch 863/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 863 || Epoch_D_Loss:0.1130 ||Epoch_G_Loss:0.1257 ||Epoch_E_Loss:0.1202\n",
            "timer:  0.4250 sec.\n",
            "-------------\n",
            "Epoch 864/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 864 || Epoch_D_Loss:0.1142 ||Epoch_G_Loss:0.1230 ||Epoch_E_Loss:0.1242\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 865/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 865 || Epoch_D_Loss:0.1145 ||Epoch_G_Loss:0.1176 ||Epoch_E_Loss:0.1247\n",
            "timer:  0.4293 sec.\n",
            "-------------\n",
            "Epoch 866/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 866 || Epoch_D_Loss:0.1190 ||Epoch_G_Loss:0.1041 ||Epoch_E_Loss:0.1247\n",
            "timer:  0.4280 sec.\n",
            "-------------\n",
            "Epoch 867/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 867 || Epoch_D_Loss:0.1301 ||Epoch_G_Loss:0.1209 ||Epoch_E_Loss:0.1092\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 868/1500\n",
            "-------------\n",
            "（train）\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "-------------\n",
            "epoch 868 || Epoch_D_Loss:0.1219 ||Epoch_G_Loss:0.1093 ||Epoch_E_Loss:0.1287\n",
            "timer:  0.4247 sec.\n",
            "-------------\n",
            "Epoch 869/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 869 || Epoch_D_Loss:0.1200 ||Epoch_G_Loss:0.1221 ||Epoch_E_Loss:0.1282\n",
            "timer:  0.4242 sec.\n",
            "-------------\n",
            "Epoch 870/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 870 || Epoch_D_Loss:0.1077 ||Epoch_G_Loss:0.1230 ||Epoch_E_Loss:0.1286\n",
            "timer:  0.4245 sec.\n",
            "-------------\n",
            "Epoch 871/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 871 || Epoch_D_Loss:0.1121 ||Epoch_G_Loss:0.1267 ||Epoch_E_Loss:0.1239\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 872/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 872 || Epoch_D_Loss:0.1188 ||Epoch_G_Loss:0.1224 ||Epoch_E_Loss:0.1216\n",
            "timer:  0.4273 sec.\n",
            "-------------\n",
            "Epoch 873/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 873 || Epoch_D_Loss:0.1166 ||Epoch_G_Loss:0.1157 ||Epoch_E_Loss:0.1245\n",
            "timer:  0.4291 sec.\n",
            "-------------\n",
            "Epoch 874/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 874 || Epoch_D_Loss:0.1205 ||Epoch_G_Loss:0.1025 ||Epoch_E_Loss:0.1203\n",
            "timer:  0.4288 sec.\n",
            "-------------\n",
            "Epoch 875/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 875 || Epoch_D_Loss:0.1227 ||Epoch_G_Loss:0.1130 ||Epoch_E_Loss:0.1142\n",
            "timer:  0.4275 sec.\n",
            "-------------\n",
            "Epoch 876/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 876 || Epoch_D_Loss:0.1119 ||Epoch_G_Loss:0.1028 ||Epoch_E_Loss:0.1307\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 877/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 877 || Epoch_D_Loss:0.1130 ||Epoch_G_Loss:0.1173 ||Epoch_E_Loss:0.1234\n",
            "timer:  0.4239 sec.\n",
            "-------------\n",
            "Epoch 878/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 878 || Epoch_D_Loss:0.1138 ||Epoch_G_Loss:0.1084 ||Epoch_E_Loss:0.1252\n",
            "timer:  0.4239 sec.\n",
            "-------------\n",
            "Epoch 879/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 879 || Epoch_D_Loss:0.1113 ||Epoch_G_Loss:0.1218 ||Epoch_E_Loss:0.1289\n",
            "timer:  0.4243 sec.\n",
            "-------------\n",
            "Epoch 880/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 880 || Epoch_D_Loss:0.1131 ||Epoch_G_Loss:0.1323 ||Epoch_E_Loss:0.1178\n",
            "timer:  0.4252 sec.\n",
            "-------------\n",
            "Epoch 881/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 881 || Epoch_D_Loss:0.1145 ||Epoch_G_Loss:0.1227 ||Epoch_E_Loss:0.1275\n",
            "timer:  0.4275 sec.\n",
            "-------------\n",
            "Epoch 882/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 882 || Epoch_D_Loss:0.1196 ||Epoch_G_Loss:0.1245 ||Epoch_E_Loss:0.1245\n",
            "timer:  0.4294 sec.\n",
            "-------------\n",
            "Epoch 883/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 883 || Epoch_D_Loss:0.1212 ||Epoch_G_Loss:0.1160 ||Epoch_E_Loss:0.1184\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 884/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 884 || Epoch_D_Loss:0.1132 ||Epoch_G_Loss:0.1225 ||Epoch_E_Loss:0.1223\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 885/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 885 || Epoch_D_Loss:0.1240 ||Epoch_G_Loss:0.1130 ||Epoch_E_Loss:0.1320\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 886/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 886 || Epoch_D_Loss:0.1213 ||Epoch_G_Loss:0.1138 ||Epoch_E_Loss:0.1234\n",
            "timer:  0.4246 sec.\n",
            "-------------\n",
            "Epoch 887/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 887 || Epoch_D_Loss:0.1196 ||Epoch_G_Loss:0.1135 ||Epoch_E_Loss:0.1194\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 888/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 888 || Epoch_D_Loss:0.1142 ||Epoch_G_Loss:0.1119 ||Epoch_E_Loss:0.1336\n",
            "timer:  0.4242 sec.\n",
            "-------------\n",
            "Epoch 889/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 889 || Epoch_D_Loss:0.1205 ||Epoch_G_Loss:0.1301 ||Epoch_E_Loss:0.1206\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 890/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 890 || Epoch_D_Loss:0.1132 ||Epoch_G_Loss:0.1319 ||Epoch_E_Loss:0.1298\n",
            "timer:  0.4294 sec.\n",
            "-------------\n",
            "Epoch 891/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 891 || Epoch_D_Loss:0.1178 ||Epoch_G_Loss:0.1066 ||Epoch_E_Loss:0.1294\n",
            "timer:  0.4299 sec.\n",
            "-------------\n",
            "Epoch 892/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 892 || Epoch_D_Loss:0.1234 ||Epoch_G_Loss:0.1151 ||Epoch_E_Loss:0.1168\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 893/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 893 || Epoch_D_Loss:0.1149 ||Epoch_G_Loss:0.1053 ||Epoch_E_Loss:0.1312\n",
            "timer:  0.4270 sec.\n",
            "-------------\n",
            "Epoch 894/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 894 || Epoch_D_Loss:0.1295 ||Epoch_G_Loss:0.1204 ||Epoch_E_Loss:0.1173\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 895/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 895 || Epoch_D_Loss:0.1234 ||Epoch_G_Loss:0.0988 ||Epoch_E_Loss:0.1230\n",
            "timer:  0.4249 sec.\n",
            "-------------\n",
            "Epoch 896/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 896 || Epoch_D_Loss:0.1232 ||Epoch_G_Loss:0.1066 ||Epoch_E_Loss:0.1156\n",
            "timer:  0.4249 sec.\n",
            "-------------\n",
            "Epoch 897/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 897 || Epoch_D_Loss:0.1191 ||Epoch_G_Loss:0.1168 ||Epoch_E_Loss:0.1237\n",
            "timer:  0.4243 sec.\n",
            "-------------\n",
            "Epoch 898/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 898 || Epoch_D_Loss:0.1168 ||Epoch_G_Loss:0.1118 ||Epoch_E_Loss:0.1283\n",
            "timer:  0.4275 sec.\n",
            "-------------\n",
            "Epoch 899/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 899 || Epoch_D_Loss:0.1159 ||Epoch_G_Loss:0.1172 ||Epoch_E_Loss:0.1233\n",
            "timer:  0.4270 sec.\n",
            "-------------\n",
            "Epoch 900/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 900 || Epoch_D_Loss:0.1246 ||Epoch_G_Loss:0.1116 ||Epoch_E_Loss:0.1159\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 901/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 901 || Epoch_D_Loss:0.1291 ||Epoch_G_Loss:0.1112 ||Epoch_E_Loss:0.1208\n",
            "timer:  0.4255 sec.\n",
            "-------------\n",
            "Epoch 902/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 902 || Epoch_D_Loss:0.1171 ||Epoch_G_Loss:0.1142 ||Epoch_E_Loss:0.1263\n",
            "timer:  0.4245 sec.\n",
            "-------------\n",
            "Epoch 903/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 903 || Epoch_D_Loss:0.1197 ||Epoch_G_Loss:0.1212 ||Epoch_E_Loss:0.1165\n",
            "timer:  0.4243 sec.\n",
            "-------------\n",
            "Epoch 904/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 904 || Epoch_D_Loss:0.1212 ||Epoch_G_Loss:0.1184 ||Epoch_E_Loss:0.1241\n",
            "timer:  0.4245 sec.\n",
            "-------------\n",
            "Epoch 905/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 905 || Epoch_D_Loss:0.1161 ||Epoch_G_Loss:0.1194 ||Epoch_E_Loss:0.1263\n",
            "timer:  0.4258 sec.\n",
            "-------------\n",
            "Epoch 906/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 906 || Epoch_D_Loss:0.1199 ||Epoch_G_Loss:0.1151 ||Epoch_E_Loss:0.1226\n",
            "timer:  0.4279 sec.\n",
            "-------------\n",
            "Epoch 907/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 907 || Epoch_D_Loss:0.1287 ||Epoch_G_Loss:0.1115 ||Epoch_E_Loss:0.1235\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 908/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 908 || Epoch_D_Loss:0.1231 ||Epoch_G_Loss:0.1058 ||Epoch_E_Loss:0.1177\n",
            "timer:  0.4254 sec.\n",
            "-------------\n",
            "Epoch 909/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 909 || Epoch_D_Loss:0.1159 ||Epoch_G_Loss:0.1220 ||Epoch_E_Loss:0.1217\n",
            "timer:  0.4254 sec.\n",
            "-------------\n",
            "Epoch 910/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 910 || Epoch_D_Loss:0.1186 ||Epoch_G_Loss:0.1077 ||Epoch_E_Loss:0.1195\n",
            "timer:  0.4245 sec.\n",
            "-------------\n",
            "Epoch 911/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 911 || Epoch_D_Loss:0.1175 ||Epoch_G_Loss:0.1248 ||Epoch_E_Loss:0.1184\n",
            "timer:  0.4248 sec.\n",
            "-------------\n",
            "Epoch 912/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 912 || Epoch_D_Loss:0.1163 ||Epoch_G_Loss:0.1051 ||Epoch_E_Loss:0.1343\n",
            "timer:  0.4241 sec.\n",
            "-------------\n",
            "Epoch 913/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 913 || Epoch_D_Loss:0.1152 ||Epoch_G_Loss:0.1158 ||Epoch_E_Loss:0.1246\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 914/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 914 || Epoch_D_Loss:0.1141 ||Epoch_G_Loss:0.1095 ||Epoch_E_Loss:0.1237\n",
            "timer:  0.4271 sec.\n",
            "-------------\n",
            "Epoch 915/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 915 || Epoch_D_Loss:0.1146 ||Epoch_G_Loss:0.1241 ||Epoch_E_Loss:0.1243\n",
            "timer:  0.4281 sec.\n",
            "-------------\n",
            "Epoch 916/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 916 || Epoch_D_Loss:0.1106 ||Epoch_G_Loss:0.1295 ||Epoch_E_Loss:0.1299\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 917/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 917 || Epoch_D_Loss:0.1120 ||Epoch_G_Loss:0.1317 ||Epoch_E_Loss:0.1335\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 918/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 918 || Epoch_D_Loss:0.1061 ||Epoch_G_Loss:0.1151 ||Epoch_E_Loss:0.1285\n",
            "timer:  0.4243 sec.\n",
            "-------------\n",
            "Epoch 919/1500\n",
            "-------------\n",
            "（train）\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "-------------\n",
            "epoch 919 || Epoch_D_Loss:0.1266 ||Epoch_G_Loss:0.1092 ||Epoch_E_Loss:0.1150\n",
            "timer:  0.4238 sec.\n",
            "-------------\n",
            "Epoch 920/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 920 || Epoch_D_Loss:0.1421 ||Epoch_G_Loss:0.0952 ||Epoch_E_Loss:0.1128\n",
            "timer:  0.4250 sec.\n",
            "-------------\n",
            "Epoch 921/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 921 || Epoch_D_Loss:0.1358 ||Epoch_G_Loss:0.1106 ||Epoch_E_Loss:0.1109\n",
            "timer:  0.4254 sec.\n",
            "-------------\n",
            "Epoch 922/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 922 || Epoch_D_Loss:0.1143 ||Epoch_G_Loss:0.1152 ||Epoch_E_Loss:0.1235\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 923/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 923 || Epoch_D_Loss:0.0997 ||Epoch_G_Loss:0.1220 ||Epoch_E_Loss:0.1367\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 924/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 924 || Epoch_D_Loss:0.0968 ||Epoch_G_Loss:0.1263 ||Epoch_E_Loss:0.1404\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 925/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 925 || Epoch_D_Loss:0.1071 ||Epoch_G_Loss:0.1257 ||Epoch_E_Loss:0.1314\n",
            "timer:  0.4252 sec.\n",
            "-------------\n",
            "Epoch 926/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 926 || Epoch_D_Loss:0.1164 ||Epoch_G_Loss:0.1198 ||Epoch_E_Loss:0.1240\n",
            "timer:  0.4246 sec.\n",
            "-------------\n",
            "Epoch 927/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 927 || Epoch_D_Loss:0.1290 ||Epoch_G_Loss:0.1152 ||Epoch_E_Loss:0.1117\n",
            "timer:  0.4278 sec.\n",
            "-------------\n",
            "Epoch 928/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 928 || Epoch_D_Loss:0.1354 ||Epoch_G_Loss:0.1044 ||Epoch_E_Loss:0.1074\n",
            "timer:  0.4241 sec.\n",
            "-------------\n",
            "Epoch 929/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 929 || Epoch_D_Loss:0.1396 ||Epoch_G_Loss:0.1043 ||Epoch_E_Loss:0.1076\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 930/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 930 || Epoch_D_Loss:0.1223 ||Epoch_G_Loss:0.1128 ||Epoch_E_Loss:0.1183\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 931/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 931 || Epoch_D_Loss:0.1157 ||Epoch_G_Loss:0.1146 ||Epoch_E_Loss:0.1211\n",
            "timer:  0.4277 sec.\n",
            "-------------\n",
            "Epoch 932/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 932 || Epoch_D_Loss:0.1076 ||Epoch_G_Loss:0.1201 ||Epoch_E_Loss:0.1296\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 933/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 933 || Epoch_D_Loss:0.1048 ||Epoch_G_Loss:0.1310 ||Epoch_E_Loss:0.1251\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 934/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 934 || Epoch_D_Loss:0.0950 ||Epoch_G_Loss:0.1197 ||Epoch_E_Loss:0.1406\n",
            "timer:  0.4242 sec.\n",
            "-------------\n",
            "Epoch 935/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 935 || Epoch_D_Loss:0.0989 ||Epoch_G_Loss:0.1216 ||Epoch_E_Loss:0.1362\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 936/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 936 || Epoch_D_Loss:0.1109 ||Epoch_G_Loss:0.1260 ||Epoch_E_Loss:0.1305\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 937/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 937 || Epoch_D_Loss:0.1208 ||Epoch_G_Loss:0.1217 ||Epoch_E_Loss:0.1153\n",
            "timer:  0.4258 sec.\n",
            "-------------\n",
            "Epoch 938/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 938 || Epoch_D_Loss:0.1254 ||Epoch_G_Loss:0.1023 ||Epoch_E_Loss:0.1219\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 939/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 939 || Epoch_D_Loss:0.1264 ||Epoch_G_Loss:0.1163 ||Epoch_E_Loss:0.1100\n",
            "timer:  0.4252 sec.\n",
            "-------------\n",
            "Epoch 940/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 940 || Epoch_D_Loss:0.1348 ||Epoch_G_Loss:0.1020 ||Epoch_E_Loss:0.1207\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 941/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 941 || Epoch_D_Loss:0.1233 ||Epoch_G_Loss:0.1137 ||Epoch_E_Loss:0.1156\n",
            "timer:  0.4281 sec.\n",
            "-------------\n",
            "Epoch 942/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 942 || Epoch_D_Loss:0.1086 ||Epoch_G_Loss:0.1236 ||Epoch_E_Loss:0.1276\n",
            "timer:  0.4243 sec.\n",
            "-------------\n",
            "Epoch 943/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 943 || Epoch_D_Loss:0.0979 ||Epoch_G_Loss:0.1307 ||Epoch_E_Loss:0.1402\n",
            "timer:  0.4238 sec.\n",
            "-------------\n",
            "Epoch 944/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 944 || Epoch_D_Loss:0.1011 ||Epoch_G_Loss:0.1323 ||Epoch_E_Loss:0.1308\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 945/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 945 || Epoch_D_Loss:0.1043 ||Epoch_G_Loss:0.1267 ||Epoch_E_Loss:0.1335\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 946/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 946 || Epoch_D_Loss:0.1107 ||Epoch_G_Loss:0.1210 ||Epoch_E_Loss:0.1285\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 947/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 947 || Epoch_D_Loss:0.1169 ||Epoch_G_Loss:0.1088 ||Epoch_E_Loss:0.1282\n",
            "timer:  0.4247 sec.\n",
            "-------------\n",
            "Epoch 948/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 948 || Epoch_D_Loss:0.1281 ||Epoch_G_Loss:0.1175 ||Epoch_E_Loss:0.1161\n",
            "timer:  0.4270 sec.\n",
            "-------------\n",
            "Epoch 949/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 949 || Epoch_D_Loss:0.1189 ||Epoch_G_Loss:0.1199 ||Epoch_E_Loss:0.1196\n",
            "timer:  0.4251 sec.\n",
            "-------------\n",
            "Epoch 950/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 950 || Epoch_D_Loss:0.1108 ||Epoch_G_Loss:0.1202 ||Epoch_E_Loss:0.1287\n",
            "timer:  0.4254 sec.\n",
            "-------------\n",
            "Epoch 951/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 951 || Epoch_D_Loss:0.1063 ||Epoch_G_Loss:0.1234 ||Epoch_E_Loss:0.1255\n",
            "timer:  0.4254 sec.\n",
            "-------------\n",
            "Epoch 952/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 952 || Epoch_D_Loss:0.1156 ||Epoch_G_Loss:0.1318 ||Epoch_E_Loss:0.1226\n",
            "timer:  0.4258 sec.\n",
            "-------------\n",
            "Epoch 953/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 953 || Epoch_D_Loss:0.1246 ||Epoch_G_Loss:0.1333 ||Epoch_E_Loss:0.1275\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 954/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 954 || Epoch_D_Loss:0.1150 ||Epoch_G_Loss:0.1210 ||Epoch_E_Loss:0.1332\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 955/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 955 || Epoch_D_Loss:0.1203 ||Epoch_G_Loss:0.1160 ||Epoch_E_Loss:0.1246\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 956/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 956 || Epoch_D_Loss:0.1143 ||Epoch_G_Loss:0.1249 ||Epoch_E_Loss:0.1227\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 957/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 957 || Epoch_D_Loss:0.1171 ||Epoch_G_Loss:0.1318 ||Epoch_E_Loss:0.1239\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 958/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 958 || Epoch_D_Loss:0.1165 ||Epoch_G_Loss:0.1237 ||Epoch_E_Loss:0.1401\n",
            "timer:  0.4250 sec.\n",
            "-------------\n",
            "Epoch 959/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 959 || Epoch_D_Loss:0.1188 ||Epoch_G_Loss:0.1115 ||Epoch_E_Loss:0.1293\n",
            "timer:  0.4251 sec.\n",
            "-------------\n",
            "Epoch 960/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 960 || Epoch_D_Loss:0.1283 ||Epoch_G_Loss:0.1238 ||Epoch_E_Loss:0.1154\n",
            "timer:  0.4250 sec.\n",
            "-------------\n",
            "Epoch 961/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 961 || Epoch_D_Loss:0.1202 ||Epoch_G_Loss:0.1056 ||Epoch_E_Loss:0.1357\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 962/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 962 || Epoch_D_Loss:0.1112 ||Epoch_G_Loss:0.1307 ||Epoch_E_Loss:0.1267\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 963/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 963 || Epoch_D_Loss:0.1095 ||Epoch_G_Loss:0.1142 ||Epoch_E_Loss:0.1456\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 964/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 964 || Epoch_D_Loss:0.1132 ||Epoch_G_Loss:0.1396 ||Epoch_E_Loss:0.1281\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 965/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 965 || Epoch_D_Loss:0.1176 ||Epoch_G_Loss:0.1131 ||Epoch_E_Loss:0.1377\n",
            "timer:  0.4258 sec.\n",
            "-------------\n",
            "Epoch 966/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 966 || Epoch_D_Loss:0.1243 ||Epoch_G_Loss:0.1085 ||Epoch_E_Loss:0.1231\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 967/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 967 || Epoch_D_Loss:0.1328 ||Epoch_G_Loss:0.1148 ||Epoch_E_Loss:0.1090\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 968/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 968 || Epoch_D_Loss:0.1300 ||Epoch_G_Loss:0.1022 ||Epoch_E_Loss:0.1229\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 969/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 969 || Epoch_D_Loss:0.1257 ||Epoch_G_Loss:0.1146 ||Epoch_E_Loss:0.1185\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 970/1500\n",
            "-------------\n",
            "（train）\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "-------------\n",
            "epoch 970 || Epoch_D_Loss:0.1163 ||Epoch_G_Loss:0.1201 ||Epoch_E_Loss:0.1243\n",
            "timer:  0.4278 sec.\n",
            "-------------\n",
            "Epoch 971/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 971 || Epoch_D_Loss:0.1190 ||Epoch_G_Loss:0.1123 ||Epoch_E_Loss:0.1283\n",
            "timer:  0.4270 sec.\n",
            "-------------\n",
            "Epoch 972/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 972 || Epoch_D_Loss:0.1257 ||Epoch_G_Loss:0.1066 ||Epoch_E_Loss:0.1243\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 973/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 973 || Epoch_D_Loss:0.1202 ||Epoch_G_Loss:0.1100 ||Epoch_E_Loss:0.1236\n",
            "timer:  0.4277 sec.\n",
            "-------------\n",
            "Epoch 974/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 974 || Epoch_D_Loss:0.1223 ||Epoch_G_Loss:0.1080 ||Epoch_E_Loss:0.1320\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 975/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 975 || Epoch_D_Loss:0.1502 ||Epoch_G_Loss:0.1045 ||Epoch_E_Loss:0.1038\n",
            "timer:  0.4278 sec.\n",
            "-------------\n",
            "Epoch 976/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 976 || Epoch_D_Loss:0.1476 ||Epoch_G_Loss:0.1002 ||Epoch_E_Loss:0.1161\n",
            "timer:  0.4283 sec.\n",
            "-------------\n",
            "Epoch 977/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 977 || Epoch_D_Loss:0.1211 ||Epoch_G_Loss:0.1141 ||Epoch_E_Loss:0.1355\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 978/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 978 || Epoch_D_Loss:0.0996 ||Epoch_G_Loss:0.1232 ||Epoch_E_Loss:0.1472\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 979/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 979 || Epoch_D_Loss:0.1035 ||Epoch_G_Loss:0.1269 ||Epoch_E_Loss:0.1375\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 980/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 980 || Epoch_D_Loss:0.1337 ||Epoch_G_Loss:0.1010 ||Epoch_E_Loss:0.1194\n",
            "timer:  0.4278 sec.\n",
            "-------------\n",
            "Epoch 981/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 981 || Epoch_D_Loss:0.1437 ||Epoch_G_Loss:0.1006 ||Epoch_E_Loss:0.1233\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 982/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 982 || Epoch_D_Loss:0.1356 ||Epoch_G_Loss:0.1154 ||Epoch_E_Loss:0.1170\n",
            "timer:  0.4292 sec.\n",
            "-------------\n",
            "Epoch 983/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 983 || Epoch_D_Loss:0.1368 ||Epoch_G_Loss:0.1026 ||Epoch_E_Loss:0.1215\n",
            "timer:  0.4371 sec.\n",
            "-------------\n",
            "Epoch 984/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 984 || Epoch_D_Loss:0.1243 ||Epoch_G_Loss:0.1120 ||Epoch_E_Loss:0.1270\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 985/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 985 || Epoch_D_Loss:0.1374 ||Epoch_G_Loss:0.0979 ||Epoch_E_Loss:0.1303\n",
            "timer:  0.4501 sec.\n",
            "-------------\n",
            "Epoch 986/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 986 || Epoch_D_Loss:0.1215 ||Epoch_G_Loss:0.1065 ||Epoch_E_Loss:0.1394\n",
            "timer:  0.4371 sec.\n",
            "-------------\n",
            "Epoch 987/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 987 || Epoch_D_Loss:0.1054 ||Epoch_G_Loss:0.1289 ||Epoch_E_Loss:0.1431\n",
            "timer:  0.4279 sec.\n",
            "-------------\n",
            "Epoch 988/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 988 || Epoch_D_Loss:0.1288 ||Epoch_G_Loss:0.1046 ||Epoch_E_Loss:0.1323\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 989/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 989 || Epoch_D_Loss:0.1615 ||Epoch_G_Loss:0.0930 ||Epoch_E_Loss:0.1019\n",
            "timer:  0.4280 sec.\n",
            "-------------\n",
            "Epoch 990/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 990 || Epoch_D_Loss:0.1409 ||Epoch_G_Loss:0.0978 ||Epoch_E_Loss:0.1218\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 991/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 991 || Epoch_D_Loss:0.1180 ||Epoch_G_Loss:0.1068 ||Epoch_E_Loss:0.1404\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 992/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 992 || Epoch_D_Loss:0.1110 ||Epoch_G_Loss:0.1126 ||Epoch_E_Loss:0.1472\n",
            "timer:  0.4254 sec.\n",
            "-------------\n",
            "Epoch 993/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 993 || Epoch_D_Loss:0.1030 ||Epoch_G_Loss:0.1294 ||Epoch_E_Loss:0.1437\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 994/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 994 || Epoch_D_Loss:0.1057 ||Epoch_G_Loss:0.1211 ||Epoch_E_Loss:0.1524\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 995/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 995 || Epoch_D_Loss:0.1182 ||Epoch_G_Loss:0.1177 ||Epoch_E_Loss:0.1387\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 996/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 996 || Epoch_D_Loss:0.1599 ||Epoch_G_Loss:0.0950 ||Epoch_E_Loss:0.1215\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 997/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 997 || Epoch_D_Loss:0.1529 ||Epoch_G_Loss:0.0941 ||Epoch_E_Loss:0.1289\n",
            "timer:  0.4293 sec.\n",
            "-------------\n",
            "Epoch 998/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 998 || Epoch_D_Loss:0.1180 ||Epoch_G_Loss:0.1164 ||Epoch_E_Loss:0.1564\n",
            "timer:  0.4279 sec.\n",
            "-------------\n",
            "Epoch 999/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 999 || Epoch_D_Loss:0.1012 ||Epoch_G_Loss:0.1322 ||Epoch_E_Loss:0.1621\n",
            "timer:  0.4271 sec.\n",
            "-------------\n",
            "Epoch 1000/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1000 || Epoch_D_Loss:0.1248 ||Epoch_G_Loss:0.1088 ||Epoch_E_Loss:0.1463\n",
            "timer:  0.4270 sec.\n",
            "-------------\n",
            "Epoch 1001/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1001 || Epoch_D_Loss:0.1414 ||Epoch_G_Loss:0.1058 ||Epoch_E_Loss:0.1380\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 1002/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1002 || Epoch_D_Loss:0.1318 ||Epoch_G_Loss:0.1067 ||Epoch_E_Loss:0.1429\n",
            "timer:  0.4240 sec.\n",
            "-------------\n",
            "Epoch 1003/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1003 || Epoch_D_Loss:0.1515 ||Epoch_G_Loss:0.1104 ||Epoch_E_Loss:0.1180\n",
            "timer:  0.4245 sec.\n",
            "-------------\n",
            "Epoch 1004/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1004 || Epoch_D_Loss:0.1540 ||Epoch_G_Loss:0.0966 ||Epoch_E_Loss:0.1161\n",
            "timer:  0.4252 sec.\n",
            "-------------\n",
            "Epoch 1005/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1005 || Epoch_D_Loss:0.1369 ||Epoch_G_Loss:0.0997 ||Epoch_E_Loss:0.1332\n",
            "timer:  0.4276 sec.\n",
            "-------------\n",
            "Epoch 1006/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1006 || Epoch_D_Loss:0.1133 ||Epoch_G_Loss:0.1372 ||Epoch_E_Loss:0.1413\n",
            "timer:  0.4276 sec.\n",
            "-------------\n",
            "Epoch 1007/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1007 || Epoch_D_Loss:0.0925 ||Epoch_G_Loss:0.1499 ||Epoch_E_Loss:0.1640\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 1008/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1008 || Epoch_D_Loss:0.1121 ||Epoch_G_Loss:0.1186 ||Epoch_E_Loss:0.1575\n",
            "timer:  0.4254 sec.\n",
            "-------------\n",
            "Epoch 1009/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1009 || Epoch_D_Loss:0.1348 ||Epoch_G_Loss:0.1096 ||Epoch_E_Loss:0.1304\n",
            "timer:  0.4235 sec.\n",
            "-------------\n",
            "Epoch 1010/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1010 || Epoch_D_Loss:0.1426 ||Epoch_G_Loss:0.0850 ||Epoch_E_Loss:0.1327\n",
            "timer:  0.4241 sec.\n",
            "-------------\n",
            "Epoch 1011/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1011 || Epoch_D_Loss:0.1482 ||Epoch_G_Loss:0.1141 ||Epoch_E_Loss:0.1180\n",
            "timer:  0.4251 sec.\n",
            "-------------\n",
            "Epoch 1012/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1012 || Epoch_D_Loss:0.1231 ||Epoch_G_Loss:0.1092 ||Epoch_E_Loss:0.1421\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 1013/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1013 || Epoch_D_Loss:0.1157 ||Epoch_G_Loss:0.1127 ||Epoch_E_Loss:0.1462\n",
            "timer:  0.4280 sec.\n",
            "-------------\n",
            "Epoch 1014/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1014 || Epoch_D_Loss:0.1120 ||Epoch_G_Loss:0.1193 ||Epoch_E_Loss:0.1523\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 1015/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1015 || Epoch_D_Loss:0.1138 ||Epoch_G_Loss:0.1289 ||Epoch_E_Loss:0.1397\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 1016/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1016 || Epoch_D_Loss:0.1069 ||Epoch_G_Loss:0.1167 ||Epoch_E_Loss:0.1451\n",
            "timer:  0.4255 sec.\n",
            "-------------\n",
            "Epoch 1017/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1017 || Epoch_D_Loss:0.1317 ||Epoch_G_Loss:0.1144 ||Epoch_E_Loss:0.1254\n",
            "timer:  0.4245 sec.\n",
            "-------------\n",
            "Epoch 1018/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1018 || Epoch_D_Loss:0.1519 ||Epoch_G_Loss:0.0829 ||Epoch_E_Loss:0.1148\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 1019/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1019 || Epoch_D_Loss:0.1497 ||Epoch_G_Loss:0.1002 ||Epoch_E_Loss:0.1215\n",
            "timer:  0.4289 sec.\n",
            "-------------\n",
            "Epoch 1020/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1020 || Epoch_D_Loss:0.1174 ||Epoch_G_Loss:0.1270 ||Epoch_E_Loss:0.1331\n",
            "timer:  0.4271 sec.\n",
            "-------------\n",
            "Epoch 1021/1500\n",
            "-------------\n",
            "（train）\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "-------------\n",
            "epoch 1021 || Epoch_D_Loss:0.1037 ||Epoch_G_Loss:0.1246 ||Epoch_E_Loss:0.1632\n",
            "timer:  0.4258 sec.\n",
            "-------------\n",
            "Epoch 1022/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1022 || Epoch_D_Loss:0.1273 ||Epoch_G_Loss:0.1109 ||Epoch_E_Loss:0.1317\n",
            "timer:  0.4273 sec.\n",
            "-------------\n",
            "Epoch 1023/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1023 || Epoch_D_Loss:0.1450 ||Epoch_G_Loss:0.1000 ||Epoch_E_Loss:0.1182\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 1024/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1024 || Epoch_D_Loss:0.1430 ||Epoch_G_Loss:0.1053 ||Epoch_E_Loss:0.1194\n",
            "timer:  0.4243 sec.\n",
            "-------------\n",
            "Epoch 1025/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1025 || Epoch_D_Loss:0.1222 ||Epoch_G_Loss:0.1216 ||Epoch_E_Loss:0.1391\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 1026/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1026 || Epoch_D_Loss:0.1197 ||Epoch_G_Loss:0.1090 ||Epoch_E_Loss:0.1348\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 1027/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1027 || Epoch_D_Loss:0.1416 ||Epoch_G_Loss:0.0974 ||Epoch_E_Loss:0.1177\n",
            "timer:  0.4275 sec.\n",
            "-------------\n",
            "Epoch 1028/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1028 || Epoch_D_Loss:0.1506 ||Epoch_G_Loss:0.0999 ||Epoch_E_Loss:0.1102\n",
            "timer:  0.4271 sec.\n",
            "-------------\n",
            "Epoch 1029/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1029 || Epoch_D_Loss:0.1364 ||Epoch_G_Loss:0.1092 ||Epoch_E_Loss:0.1112\n",
            "timer:  0.4275 sec.\n",
            "-------------\n",
            "Epoch 1030/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1030 || Epoch_D_Loss:0.1137 ||Epoch_G_Loss:0.1076 ||Epoch_E_Loss:0.1376\n",
            "timer:  0.4294 sec.\n",
            "-------------\n",
            "Epoch 1031/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1031 || Epoch_D_Loss:0.1035 ||Epoch_G_Loss:0.1280 ||Epoch_E_Loss:0.1361\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 1032/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1032 || Epoch_D_Loss:0.1177 ||Epoch_G_Loss:0.1132 ||Epoch_E_Loss:0.1257\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 1033/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1033 || Epoch_D_Loss:0.1418 ||Epoch_G_Loss:0.1002 ||Epoch_E_Loss:0.1064\n",
            "timer:  0.4276 sec.\n",
            "-------------\n",
            "Epoch 1034/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1034 || Epoch_D_Loss:0.1354 ||Epoch_G_Loss:0.0905 ||Epoch_E_Loss:0.1220\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 1035/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1035 || Epoch_D_Loss:0.1236 ||Epoch_G_Loss:0.1206 ||Epoch_E_Loss:0.1282\n",
            "timer:  0.4278 sec.\n",
            "-------------\n",
            "Epoch 1036/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1036 || Epoch_D_Loss:0.1046 ||Epoch_G_Loss:0.1109 ||Epoch_E_Loss:0.1443\n",
            "timer:  0.4284 sec.\n",
            "-------------\n",
            "Epoch 1037/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1037 || Epoch_D_Loss:0.1071 ||Epoch_G_Loss:0.1157 ||Epoch_E_Loss:0.1346\n",
            "timer:  0.4292 sec.\n",
            "-------------\n",
            "Epoch 1038/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1038 || Epoch_D_Loss:0.1130 ||Epoch_G_Loss:0.1123 ||Epoch_E_Loss:0.1288\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 1039/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1039 || Epoch_D_Loss:0.1261 ||Epoch_G_Loss:0.0999 ||Epoch_E_Loss:0.1292\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 1040/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1040 || Epoch_D_Loss:0.1250 ||Epoch_G_Loss:0.1209 ||Epoch_E_Loss:0.1142\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 1041/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1041 || Epoch_D_Loss:0.1081 ||Epoch_G_Loss:0.1166 ||Epoch_E_Loss:0.1429\n",
            "timer:  0.4275 sec.\n",
            "-------------\n",
            "Epoch 1042/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1042 || Epoch_D_Loss:0.0911 ||Epoch_G_Loss:0.1449 ||Epoch_E_Loss:0.1451\n",
            "timer:  0.4279 sec.\n",
            "-------------\n",
            "Epoch 1043/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1043 || Epoch_D_Loss:0.0896 ||Epoch_G_Loss:0.1271 ||Epoch_E_Loss:0.1513\n",
            "timer:  0.4300 sec.\n",
            "-------------\n",
            "Epoch 1044/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1044 || Epoch_D_Loss:0.1076 ||Epoch_G_Loss:0.1185 ||Epoch_E_Loss:0.1352\n",
            "timer:  0.4292 sec.\n",
            "-------------\n",
            "Epoch 1045/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1045 || Epoch_D_Loss:0.1289 ||Epoch_G_Loss:0.1113 ||Epoch_E_Loss:0.1133\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 1046/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1046 || Epoch_D_Loss:0.1529 ||Epoch_G_Loss:0.0909 ||Epoch_E_Loss:0.1098\n",
            "timer:  0.4255 sec.\n",
            "-------------\n",
            "Epoch 1047/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1047 || Epoch_D_Loss:0.1322 ||Epoch_G_Loss:0.1065 ||Epoch_E_Loss:0.1138\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 1048/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1048 || Epoch_D_Loss:0.0914 ||Epoch_G_Loss:0.1432 ||Epoch_E_Loss:0.1523\n",
            "timer:  0.4252 sec.\n",
            "-------------\n",
            "Epoch 1049/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1049 || Epoch_D_Loss:0.0744 ||Epoch_G_Loss:0.1611 ||Epoch_E_Loss:0.1660\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 1050/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1050 || Epoch_D_Loss:0.0856 ||Epoch_G_Loss:0.1436 ||Epoch_E_Loss:0.1496\n",
            "timer:  0.4283 sec.\n",
            "-------------\n",
            "Epoch 1051/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1051 || Epoch_D_Loss:0.1174 ||Epoch_G_Loss:0.1136 ||Epoch_E_Loss:0.1209\n",
            "timer:  0.4271 sec.\n",
            "-------------\n",
            "Epoch 1052/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1052 || Epoch_D_Loss:0.1476 ||Epoch_G_Loss:0.0934 ||Epoch_E_Loss:0.1123\n",
            "timer:  0.4314 sec.\n",
            "-------------\n",
            "Epoch 1053/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1053 || Epoch_D_Loss:0.1232 ||Epoch_G_Loss:0.1070 ||Epoch_E_Loss:0.1229\n",
            "timer:  0.4276 sec.\n",
            "-------------\n",
            "Epoch 1054/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1054 || Epoch_D_Loss:0.1045 ||Epoch_G_Loss:0.1249 ||Epoch_E_Loss:0.1401\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 1055/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1055 || Epoch_D_Loss:0.1054 ||Epoch_G_Loss:0.1209 ||Epoch_E_Loss:0.1353\n",
            "timer:  0.4240 sec.\n",
            "-------------\n",
            "Epoch 1056/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1056 || Epoch_D_Loss:0.1191 ||Epoch_G_Loss:0.1301 ||Epoch_E_Loss:0.1266\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 1057/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1057 || Epoch_D_Loss:0.1061 ||Epoch_G_Loss:0.1130 ||Epoch_E_Loss:0.1458\n",
            "timer:  0.4286 sec.\n",
            "-------------\n",
            "Epoch 1058/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1058 || Epoch_D_Loss:0.0959 ||Epoch_G_Loss:0.1330 ||Epoch_E_Loss:0.1487\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 1059/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1059 || Epoch_D_Loss:0.0928 ||Epoch_G_Loss:0.1477 ||Epoch_E_Loss:0.1521\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 1060/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1060 || Epoch_D_Loss:0.0987 ||Epoch_G_Loss:0.1306 ||Epoch_E_Loss:0.1452\n",
            "timer:  0.4275 sec.\n",
            "-------------\n",
            "Epoch 1061/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1061 || Epoch_D_Loss:0.1324 ||Epoch_G_Loss:0.1074 ||Epoch_E_Loss:0.1140\n",
            "timer:  0.4246 sec.\n",
            "-------------\n",
            "Epoch 1062/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1062 || Epoch_D_Loss:0.1624 ||Epoch_G_Loss:0.0900 ||Epoch_E_Loss:0.1065\n",
            "timer:  0.4249 sec.\n",
            "-------------\n",
            "Epoch 1063/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1063 || Epoch_D_Loss:0.1255 ||Epoch_G_Loss:0.1162 ||Epoch_E_Loss:0.1278\n",
            "timer:  0.4254 sec.\n",
            "-------------\n",
            "Epoch 1064/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1064 || Epoch_D_Loss:0.0991 ||Epoch_G_Loss:0.1276 ||Epoch_E_Loss:0.1473\n",
            "timer:  0.4295 sec.\n",
            "-------------\n",
            "Epoch 1065/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1065 || Epoch_D_Loss:0.0902 ||Epoch_G_Loss:0.1461 ||Epoch_E_Loss:0.1574\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 1066/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1066 || Epoch_D_Loss:0.0895 ||Epoch_G_Loss:0.1341 ||Epoch_E_Loss:0.1572\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 1067/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1067 || Epoch_D_Loss:0.1147 ||Epoch_G_Loss:0.1355 ||Epoch_E_Loss:0.1273\n",
            "timer:  0.4279 sec.\n",
            "-------------\n",
            "Epoch 1068/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1068 || Epoch_D_Loss:0.1293 ||Epoch_G_Loss:0.1202 ||Epoch_E_Loss:0.1275\n",
            "timer:  0.4300 sec.\n",
            "-------------\n",
            "Epoch 1069/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1069 || Epoch_D_Loss:0.1215 ||Epoch_G_Loss:0.1061 ||Epoch_E_Loss:0.1360\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 1070/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1070 || Epoch_D_Loss:0.1160 ||Epoch_G_Loss:0.1306 ||Epoch_E_Loss:0.1372\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 1071/1500\n",
            "-------------\n",
            "（train）\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "-------------\n",
            "epoch 1071 || Epoch_D_Loss:0.1043 ||Epoch_G_Loss:0.1259 ||Epoch_E_Loss:0.1457\n",
            "timer:  0.4279 sec.\n",
            "-------------\n",
            "Epoch 1072/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1072 || Epoch_D_Loss:0.1133 ||Epoch_G_Loss:0.1194 ||Epoch_E_Loss:0.1308\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 1073/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1073 || Epoch_D_Loss:0.1391 ||Epoch_G_Loss:0.1070 ||Epoch_E_Loss:0.1279\n",
            "timer:  0.4245 sec.\n",
            "-------------\n",
            "Epoch 1074/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1074 || Epoch_D_Loss:0.1454 ||Epoch_G_Loss:0.1005 ||Epoch_E_Loss:0.1081\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 1075/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1075 || Epoch_D_Loss:0.1097 ||Epoch_G_Loss:0.1182 ||Epoch_E_Loss:0.1419\n",
            "timer:  0.4290 sec.\n",
            "-------------\n",
            "Epoch 1076/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1076 || Epoch_D_Loss:0.0790 ||Epoch_G_Loss:0.1449 ||Epoch_E_Loss:0.1686\n",
            "timer:  0.4280 sec.\n",
            "-------------\n",
            "Epoch 1077/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1077 || Epoch_D_Loss:0.0945 ||Epoch_G_Loss:0.1449 ||Epoch_E_Loss:0.1478\n",
            "timer:  0.4281 sec.\n",
            "-------------\n",
            "Epoch 1078/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1078 || Epoch_D_Loss:0.1169 ||Epoch_G_Loss:0.1093 ||Epoch_E_Loss:0.1241\n",
            "timer:  0.4282 sec.\n",
            "-------------\n",
            "Epoch 1079/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1079 || Epoch_D_Loss:0.1364 ||Epoch_G_Loss:0.1036 ||Epoch_E_Loss:0.1072\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 1080/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1080 || Epoch_D_Loss:0.1411 ||Epoch_G_Loss:0.1029 ||Epoch_E_Loss:0.1155\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 1081/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1081 || Epoch_D_Loss:0.1163 ||Epoch_G_Loss:0.1194 ||Epoch_E_Loss:0.1231\n",
            "timer:  0.4258 sec.\n",
            "-------------\n",
            "Epoch 1082/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1082 || Epoch_D_Loss:0.1033 ||Epoch_G_Loss:0.1222 ||Epoch_E_Loss:0.1388\n",
            "timer:  0.4252 sec.\n",
            "-------------\n",
            "Epoch 1083/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1083 || Epoch_D_Loss:0.0978 ||Epoch_G_Loss:0.1342 ||Epoch_E_Loss:0.1412\n",
            "timer:  0.4244 sec.\n",
            "-------------\n",
            "Epoch 1084/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1084 || Epoch_D_Loss:0.0963 ||Epoch_G_Loss:0.1296 ||Epoch_E_Loss:0.1370\n",
            "timer:  0.4271 sec.\n",
            "-------------\n",
            "Epoch 1085/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1085 || Epoch_D_Loss:0.1070 ||Epoch_G_Loss:0.1133 ||Epoch_E_Loss:0.1380\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 1086/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1086 || Epoch_D_Loss:0.1223 ||Epoch_G_Loss:0.1164 ||Epoch_E_Loss:0.1194\n",
            "timer:  0.4279 sec.\n",
            "-------------\n",
            "Epoch 1087/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1087 || Epoch_D_Loss:0.1185 ||Epoch_G_Loss:0.1067 ||Epoch_E_Loss:0.1264\n",
            "timer:  0.4276 sec.\n",
            "-------------\n",
            "Epoch 1088/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1088 || Epoch_D_Loss:0.1175 ||Epoch_G_Loss:0.1121 ||Epoch_E_Loss:0.1299\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 1089/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1089 || Epoch_D_Loss:0.1029 ||Epoch_G_Loss:0.1357 ||Epoch_E_Loss:0.1330\n",
            "timer:  0.4241 sec.\n",
            "-------------\n",
            "Epoch 1090/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1090 || Epoch_D_Loss:0.1002 ||Epoch_G_Loss:0.1427 ||Epoch_E_Loss:0.1377\n",
            "timer:  0.4242 sec.\n",
            "-------------\n",
            "Epoch 1091/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1091 || Epoch_D_Loss:0.1010 ||Epoch_G_Loss:0.1237 ||Epoch_E_Loss:0.1440\n",
            "timer:  0.4239 sec.\n",
            "-------------\n",
            "Epoch 1092/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1092 || Epoch_D_Loss:0.1129 ||Epoch_G_Loss:0.1226 ||Epoch_E_Loss:0.1270\n",
            "timer:  0.4292 sec.\n",
            "-------------\n",
            "Epoch 1093/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1093 || Epoch_D_Loss:0.1157 ||Epoch_G_Loss:0.1057 ||Epoch_E_Loss:0.1289\n",
            "timer:  0.4277 sec.\n",
            "-------------\n",
            "Epoch 1094/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1094 || Epoch_D_Loss:0.1164 ||Epoch_G_Loss:0.1215 ||Epoch_E_Loss:0.1313\n",
            "timer:  0.4294 sec.\n",
            "-------------\n",
            "Epoch 1095/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1095 || Epoch_D_Loss:0.1090 ||Epoch_G_Loss:0.1267 ||Epoch_E_Loss:0.1311\n",
            "timer:  0.4270 sec.\n",
            "-------------\n",
            "Epoch 1096/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1096 || Epoch_D_Loss:0.1218 ||Epoch_G_Loss:0.1116 ||Epoch_E_Loss:0.1344\n",
            "timer:  0.4246 sec.\n",
            "-------------\n",
            "Epoch 1097/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1097 || Epoch_D_Loss:0.1160 ||Epoch_G_Loss:0.1159 ||Epoch_E_Loss:0.1286\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 1098/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1098 || Epoch_D_Loss:0.1115 ||Epoch_G_Loss:0.1182 ||Epoch_E_Loss:0.1359\n",
            "timer:  0.4287 sec.\n",
            "-------------\n",
            "Epoch 1099/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1099 || Epoch_D_Loss:0.1061 ||Epoch_G_Loss:0.1369 ||Epoch_E_Loss:0.1284\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 1100/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1100 || Epoch_D_Loss:0.1098 ||Epoch_G_Loss:0.1138 ||Epoch_E_Loss:0.1367\n",
            "timer:  0.4280 sec.\n",
            "-------------\n",
            "Epoch 1101/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1101 || Epoch_D_Loss:0.1147 ||Epoch_G_Loss:0.1109 ||Epoch_E_Loss:0.1285\n",
            "timer:  0.4277 sec.\n",
            "-------------\n",
            "Epoch 1102/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1102 || Epoch_D_Loss:0.1150 ||Epoch_G_Loss:0.1129 ||Epoch_E_Loss:0.1285\n",
            "timer:  0.4275 sec.\n",
            "-------------\n",
            "Epoch 1103/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1103 || Epoch_D_Loss:0.1123 ||Epoch_G_Loss:0.1222 ||Epoch_E_Loss:0.1291\n",
            "timer:  0.4246 sec.\n",
            "-------------\n",
            "Epoch 1104/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1104 || Epoch_D_Loss:0.1083 ||Epoch_G_Loss:0.1275 ||Epoch_E_Loss:0.1307\n",
            "timer:  0.4242 sec.\n",
            "-------------\n",
            "Epoch 1105/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1105 || Epoch_D_Loss:0.1059 ||Epoch_G_Loss:0.1269 ||Epoch_E_Loss:0.1350\n",
            "timer:  0.4247 sec.\n",
            "-------------\n",
            "Epoch 1106/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1106 || Epoch_D_Loss:0.1068 ||Epoch_G_Loss:0.1152 ||Epoch_E_Loss:0.1335\n",
            "timer:  0.4282 sec.\n",
            "-------------\n",
            "Epoch 1107/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1107 || Epoch_D_Loss:0.1117 ||Epoch_G_Loss:0.1142 ||Epoch_E_Loss:0.1336\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 1108/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1108 || Epoch_D_Loss:0.1180 ||Epoch_G_Loss:0.1276 ||Epoch_E_Loss:0.1249\n",
            "timer:  0.4297 sec.\n",
            "-------------\n",
            "Epoch 1109/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1109 || Epoch_D_Loss:0.1143 ||Epoch_G_Loss:0.1167 ||Epoch_E_Loss:0.1313\n",
            "timer:  0.4270 sec.\n",
            "-------------\n",
            "Epoch 1110/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1110 || Epoch_D_Loss:0.1160 ||Epoch_G_Loss:0.1144 ||Epoch_E_Loss:0.1271\n",
            "timer:  0.4253 sec.\n",
            "-------------\n",
            "Epoch 1111/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1111 || Epoch_D_Loss:0.1077 ||Epoch_G_Loss:0.1227 ||Epoch_E_Loss:0.1308\n",
            "timer:  0.4246 sec.\n",
            "-------------\n",
            "Epoch 1112/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1112 || Epoch_D_Loss:0.1061 ||Epoch_G_Loss:0.1255 ||Epoch_E_Loss:0.1388\n",
            "timer:  0.4244 sec.\n",
            "-------------\n",
            "Epoch 1113/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1113 || Epoch_D_Loss:0.1017 ||Epoch_G_Loss:0.1218 ||Epoch_E_Loss:0.1424\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 1114/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1114 || Epoch_D_Loss:0.1075 ||Epoch_G_Loss:0.1219 ||Epoch_E_Loss:0.1369\n",
            "timer:  0.4301 sec.\n",
            "-------------\n",
            "Epoch 1115/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1115 || Epoch_D_Loss:0.1135 ||Epoch_G_Loss:0.1194 ||Epoch_E_Loss:0.1280\n",
            "timer:  0.4293 sec.\n",
            "-------------\n",
            "Epoch 1116/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1116 || Epoch_D_Loss:0.1235 ||Epoch_G_Loss:0.1110 ||Epoch_E_Loss:0.1179\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 1117/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1117 || Epoch_D_Loss:0.1377 ||Epoch_G_Loss:0.0981 ||Epoch_E_Loss:0.1187\n",
            "timer:  0.4287 sec.\n",
            "-------------\n",
            "Epoch 1118/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1118 || Epoch_D_Loss:0.1256 ||Epoch_G_Loss:0.1112 ||Epoch_E_Loss:0.1198\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 1119/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1119 || Epoch_D_Loss:0.1021 ||Epoch_G_Loss:0.1273 ||Epoch_E_Loss:0.1359\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 1120/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1120 || Epoch_D_Loss:0.0926 ||Epoch_G_Loss:0.1348 ||Epoch_E_Loss:0.1446\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 1121/1500\n",
            "-------------\n",
            "（train）\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "-------------\n",
            "epoch 1121 || Epoch_D_Loss:0.0925 ||Epoch_G_Loss:0.1346 ||Epoch_E_Loss:0.1439\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 1122/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1122 || Epoch_D_Loss:0.1015 ||Epoch_G_Loss:0.1219 ||Epoch_E_Loss:0.1337\n",
            "timer:  0.4279 sec.\n",
            "-------------\n",
            "Epoch 1123/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1123 || Epoch_D_Loss:0.1165 ||Epoch_G_Loss:0.1227 ||Epoch_E_Loss:0.1186\n",
            "timer:  0.4281 sec.\n",
            "-------------\n",
            "Epoch 1124/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1124 || Epoch_D_Loss:0.1213 ||Epoch_G_Loss:0.1195 ||Epoch_E_Loss:0.1267\n",
            "timer:  0.4301 sec.\n",
            "-------------\n",
            "Epoch 1125/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1125 || Epoch_D_Loss:0.1068 ||Epoch_G_Loss:0.1184 ||Epoch_E_Loss:0.1289\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 1126/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1126 || Epoch_D_Loss:0.0997 ||Epoch_G_Loss:0.1239 ||Epoch_E_Loss:0.1359\n",
            "timer:  0.4248 sec.\n",
            "-------------\n",
            "Epoch 1127/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1127 || Epoch_D_Loss:0.1038 ||Epoch_G_Loss:0.1319 ||Epoch_E_Loss:0.1290\n",
            "timer:  0.4247 sec.\n",
            "-------------\n",
            "Epoch 1128/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1128 || Epoch_D_Loss:0.1062 ||Epoch_G_Loss:0.1298 ||Epoch_E_Loss:0.1332\n",
            "timer:  0.4275 sec.\n",
            "-------------\n",
            "Epoch 1129/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1129 || Epoch_D_Loss:0.1093 ||Epoch_G_Loss:0.1256 ||Epoch_E_Loss:0.1356\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 1130/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1130 || Epoch_D_Loss:0.1093 ||Epoch_G_Loss:0.1176 ||Epoch_E_Loss:0.1390\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 1131/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1131 || Epoch_D_Loss:0.1071 ||Epoch_G_Loss:0.1157 ||Epoch_E_Loss:0.1344\n",
            "timer:  0.4285 sec.\n",
            "-------------\n",
            "Epoch 1132/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1132 || Epoch_D_Loss:0.1138 ||Epoch_G_Loss:0.1287 ||Epoch_E_Loss:0.1317\n",
            "timer:  0.4248 sec.\n",
            "-------------\n",
            "Epoch 1133/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1133 || Epoch_D_Loss:0.1137 ||Epoch_G_Loss:0.1246 ||Epoch_E_Loss:0.1234\n",
            "timer:  0.4250 sec.\n",
            "-------------\n",
            "Epoch 1134/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1134 || Epoch_D_Loss:0.1187 ||Epoch_G_Loss:0.1132 ||Epoch_E_Loss:0.1264\n",
            "timer:  0.4286 sec.\n",
            "-------------\n",
            "Epoch 1135/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1135 || Epoch_D_Loss:0.1078 ||Epoch_G_Loss:0.1178 ||Epoch_E_Loss:0.1316\n",
            "timer:  0.4252 sec.\n",
            "-------------\n",
            "Epoch 1136/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1136 || Epoch_D_Loss:0.1016 ||Epoch_G_Loss:0.1263 ||Epoch_E_Loss:0.1330\n",
            "timer:  0.4276 sec.\n",
            "-------------\n",
            "Epoch 1137/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1137 || Epoch_D_Loss:0.0988 ||Epoch_G_Loss:0.1411 ||Epoch_E_Loss:0.1369\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 1138/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1138 || Epoch_D_Loss:0.1045 ||Epoch_G_Loss:0.1251 ||Epoch_E_Loss:0.1319\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 1139/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1139 || Epoch_D_Loss:0.1105 ||Epoch_G_Loss:0.1195 ||Epoch_E_Loss:0.1251\n",
            "timer:  0.4291 sec.\n",
            "-------------\n",
            "Epoch 1140/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1140 || Epoch_D_Loss:0.1129 ||Epoch_G_Loss:0.1169 ||Epoch_E_Loss:0.1266\n",
            "timer:  0.4279 sec.\n",
            "-------------\n",
            "Epoch 1141/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1141 || Epoch_D_Loss:0.1119 ||Epoch_G_Loss:0.1215 ||Epoch_E_Loss:0.1218\n",
            "timer:  0.4270 sec.\n",
            "-------------\n",
            "Epoch 1142/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1142 || Epoch_D_Loss:0.1148 ||Epoch_G_Loss:0.1121 ||Epoch_E_Loss:0.1359\n",
            "timer:  0.4244 sec.\n",
            "-------------\n",
            "Epoch 1143/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1143 || Epoch_D_Loss:0.1138 ||Epoch_G_Loss:0.1308 ||Epoch_E_Loss:0.1252\n",
            "timer:  0.4255 sec.\n",
            "-------------\n",
            "Epoch 1144/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1144 || Epoch_D_Loss:0.1032 ||Epoch_G_Loss:0.1204 ||Epoch_E_Loss:0.1356\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 1145/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1145 || Epoch_D_Loss:0.1002 ||Epoch_G_Loss:0.1361 ||Epoch_E_Loss:0.1366\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 1146/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1146 || Epoch_D_Loss:0.1007 ||Epoch_G_Loss:0.1314 ||Epoch_E_Loss:0.1386\n",
            "timer:  0.4285 sec.\n",
            "-------------\n",
            "Epoch 1147/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1147 || Epoch_D_Loss:0.1017 ||Epoch_G_Loss:0.1352 ||Epoch_E_Loss:0.1347\n",
            "timer:  0.4295 sec.\n",
            "-------------\n",
            "Epoch 1148/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1148 || Epoch_D_Loss:0.1055 ||Epoch_G_Loss:0.1270 ||Epoch_E_Loss:0.1265\n",
            "timer:  0.4281 sec.\n",
            "-------------\n",
            "Epoch 1149/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1149 || Epoch_D_Loss:0.1099 ||Epoch_G_Loss:0.1269 ||Epoch_E_Loss:0.1263\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 1150/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1150 || Epoch_D_Loss:0.1149 ||Epoch_G_Loss:0.1085 ||Epoch_E_Loss:0.1299\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 1151/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1151 || Epoch_D_Loss:0.1113 ||Epoch_G_Loss:0.1097 ||Epoch_E_Loss:0.1271\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 1152/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1152 || Epoch_D_Loss:0.1122 ||Epoch_G_Loss:0.1183 ||Epoch_E_Loss:0.1258\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 1153/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1153 || Epoch_D_Loss:0.1118 ||Epoch_G_Loss:0.1261 ||Epoch_E_Loss:0.1268\n",
            "timer:  0.4273 sec.\n",
            "-------------\n",
            "Epoch 1154/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1154 || Epoch_D_Loss:0.1037 ||Epoch_G_Loss:0.1334 ||Epoch_E_Loss:0.1357\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 1155/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1155 || Epoch_D_Loss:0.0945 ||Epoch_G_Loss:0.1342 ||Epoch_E_Loss:0.1366\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 1156/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1156 || Epoch_D_Loss:0.0998 ||Epoch_G_Loss:0.1369 ||Epoch_E_Loss:0.1371\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 1157/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1157 || Epoch_D_Loss:0.0982 ||Epoch_G_Loss:0.1409 ||Epoch_E_Loss:0.1365\n",
            "timer:  0.4281 sec.\n",
            "-------------\n",
            "Epoch 1158/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1158 || Epoch_D_Loss:0.1073 ||Epoch_G_Loss:0.1256 ||Epoch_E_Loss:0.1344\n",
            "timer:  0.4258 sec.\n",
            "-------------\n",
            "Epoch 1159/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1159 || Epoch_D_Loss:0.1107 ||Epoch_G_Loss:0.1180 ||Epoch_E_Loss:0.1252\n",
            "timer:  0.4250 sec.\n",
            "-------------\n",
            "Epoch 1160/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1160 || Epoch_D_Loss:0.1147 ||Epoch_G_Loss:0.1217 ||Epoch_E_Loss:0.1222\n",
            "timer:  0.4247 sec.\n",
            "-------------\n",
            "Epoch 1161/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1161 || Epoch_D_Loss:0.1090 ||Epoch_G_Loss:0.1155 ||Epoch_E_Loss:0.1256\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 1162/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1162 || Epoch_D_Loss:0.1056 ||Epoch_G_Loss:0.1161 ||Epoch_E_Loss:0.1355\n",
            "timer:  0.4284 sec.\n",
            "-------------\n",
            "Epoch 1163/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1163 || Epoch_D_Loss:0.1015 ||Epoch_G_Loss:0.1379 ||Epoch_E_Loss:0.1329\n",
            "timer:  0.4283 sec.\n",
            "-------------\n",
            "Epoch 1164/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1164 || Epoch_D_Loss:0.1070 ||Epoch_G_Loss:0.1234 ||Epoch_E_Loss:0.1374\n",
            "timer:  0.4279 sec.\n",
            "-------------\n",
            "Epoch 1165/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1165 || Epoch_D_Loss:0.1021 ||Epoch_G_Loss:0.1330 ||Epoch_E_Loss:0.1371\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 1166/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1166 || Epoch_D_Loss:0.1022 ||Epoch_G_Loss:0.1217 ||Epoch_E_Loss:0.1384\n",
            "timer:  0.4255 sec.\n",
            "-------------\n",
            "Epoch 1167/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1167 || Epoch_D_Loss:0.1077 ||Epoch_G_Loss:0.1361 ||Epoch_E_Loss:0.1300\n",
            "timer:  0.4248 sec.\n",
            "-------------\n",
            "Epoch 1168/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1168 || Epoch_D_Loss:0.1017 ||Epoch_G_Loss:0.1401 ||Epoch_E_Loss:0.1352\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 1169/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1169 || Epoch_D_Loss:0.1080 ||Epoch_G_Loss:0.1218 ||Epoch_E_Loss:0.1316\n",
            "timer:  0.4294 sec.\n",
            "-------------\n",
            "Epoch 1170/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1170 || Epoch_D_Loss:0.1135 ||Epoch_G_Loss:0.1160 ||Epoch_E_Loss:0.1226\n",
            "timer:  0.4321 sec.\n",
            "-------------\n",
            "Epoch 1171/1500\n",
            "-------------\n",
            "（train）\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "-------------\n",
            "epoch 1171 || Epoch_D_Loss:0.1080 ||Epoch_G_Loss:0.1089 ||Epoch_E_Loss:0.1350\n",
            "timer:  0.4291 sec.\n",
            "-------------\n",
            "Epoch 1172/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1172 || Epoch_D_Loss:0.1110 ||Epoch_G_Loss:0.1298 ||Epoch_E_Loss:0.1231\n",
            "timer:  0.4286 sec.\n",
            "-------------\n",
            "Epoch 1173/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1173 || Epoch_D_Loss:0.0974 ||Epoch_G_Loss:0.1247 ||Epoch_E_Loss:0.1480\n",
            "timer:  0.4254 sec.\n",
            "-------------\n",
            "Epoch 1174/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1174 || Epoch_D_Loss:0.0903 ||Epoch_G_Loss:0.1285 ||Epoch_E_Loss:0.1443\n",
            "timer:  0.4287 sec.\n",
            "-------------\n",
            "Epoch 1175/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1175 || Epoch_D_Loss:0.0947 ||Epoch_G_Loss:0.1430 ||Epoch_E_Loss:0.1399\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 1176/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1176 || Epoch_D_Loss:0.0924 ||Epoch_G_Loss:0.1351 ||Epoch_E_Loss:0.1416\n",
            "timer:  0.4310 sec.\n",
            "-------------\n",
            "Epoch 1177/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1177 || Epoch_D_Loss:0.1043 ||Epoch_G_Loss:0.1360 ||Epoch_E_Loss:0.1351\n",
            "timer:  0.4281 sec.\n",
            "-------------\n",
            "Epoch 1178/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1178 || Epoch_D_Loss:0.1038 ||Epoch_G_Loss:0.1240 ||Epoch_E_Loss:0.1312\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 1179/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1179 || Epoch_D_Loss:0.1158 ||Epoch_G_Loss:0.1222 ||Epoch_E_Loss:0.1254\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 1180/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1180 || Epoch_D_Loss:0.1141 ||Epoch_G_Loss:0.1249 ||Epoch_E_Loss:0.1270\n",
            "timer:  0.4258 sec.\n",
            "-------------\n",
            "Epoch 1181/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1181 || Epoch_D_Loss:0.1104 ||Epoch_G_Loss:0.1378 ||Epoch_E_Loss:0.1290\n",
            "timer:  0.4273 sec.\n",
            "-------------\n",
            "Epoch 1182/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1182 || Epoch_D_Loss:0.1022 ||Epoch_G_Loss:0.1255 ||Epoch_E_Loss:0.1372\n",
            "timer:  0.4285 sec.\n",
            "-------------\n",
            "Epoch 1183/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1183 || Epoch_D_Loss:0.1009 ||Epoch_G_Loss:0.1411 ||Epoch_E_Loss:0.1310\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 1184/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1184 || Epoch_D_Loss:0.0933 ||Epoch_G_Loss:0.1380 ||Epoch_E_Loss:0.1422\n",
            "timer:  0.4243 sec.\n",
            "-------------\n",
            "Epoch 1185/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1185 || Epoch_D_Loss:0.1020 ||Epoch_G_Loss:0.1395 ||Epoch_E_Loss:0.1377\n",
            "timer:  0.4255 sec.\n",
            "-------------\n",
            "Epoch 1186/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1186 || Epoch_D_Loss:0.1002 ||Epoch_G_Loss:0.1384 ||Epoch_E_Loss:0.1328\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 1187/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1187 || Epoch_D_Loss:0.1008 ||Epoch_G_Loss:0.1202 ||Epoch_E_Loss:0.1397\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 1188/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1188 || Epoch_D_Loss:0.1088 ||Epoch_G_Loss:0.1258 ||Epoch_E_Loss:0.1248\n",
            "timer:  0.4283 sec.\n",
            "-------------\n",
            "Epoch 1189/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1189 || Epoch_D_Loss:0.1090 ||Epoch_G_Loss:0.1319 ||Epoch_E_Loss:0.1243\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 1190/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1190 || Epoch_D_Loss:0.1105 ||Epoch_G_Loss:0.1290 ||Epoch_E_Loss:0.1348\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 1191/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1191 || Epoch_D_Loss:0.1019 ||Epoch_G_Loss:0.1331 ||Epoch_E_Loss:0.1287\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 1192/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1192 || Epoch_D_Loss:0.0975 ||Epoch_G_Loss:0.1347 ||Epoch_E_Loss:0.1321\n",
            "timer:  0.4275 sec.\n",
            "-------------\n",
            "Epoch 1193/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1193 || Epoch_D_Loss:0.0954 ||Epoch_G_Loss:0.1320 ||Epoch_E_Loss:0.1367\n",
            "timer:  0.4243 sec.\n",
            "-------------\n",
            "Epoch 1194/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1194 || Epoch_D_Loss:0.1087 ||Epoch_G_Loss:0.1189 ||Epoch_E_Loss:0.1353\n",
            "timer:  0.4275 sec.\n",
            "-------------\n",
            "Epoch 1195/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1195 || Epoch_D_Loss:0.1056 ||Epoch_G_Loss:0.1223 ||Epoch_E_Loss:0.1314\n",
            "timer:  0.4282 sec.\n",
            "-------------\n",
            "Epoch 1196/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1196 || Epoch_D_Loss:0.0993 ||Epoch_G_Loss:0.1284 ||Epoch_E_Loss:0.1347\n",
            "timer:  0.4288 sec.\n",
            "-------------\n",
            "Epoch 1197/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1197 || Epoch_D_Loss:0.0940 ||Epoch_G_Loss:0.1275 ||Epoch_E_Loss:0.1414\n",
            "timer:  0.4290 sec.\n",
            "-------------\n",
            "Epoch 1198/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1198 || Epoch_D_Loss:0.0975 ||Epoch_G_Loss:0.1430 ||Epoch_E_Loss:0.1326\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 1199/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1199 || Epoch_D_Loss:0.0992 ||Epoch_G_Loss:0.1415 ||Epoch_E_Loss:0.1332\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 1200/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1200 || Epoch_D_Loss:0.1086 ||Epoch_G_Loss:0.1198 ||Epoch_E_Loss:0.1421\n",
            "timer:  0.4283 sec.\n",
            "-------------\n",
            "Epoch 1201/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1201 || Epoch_D_Loss:0.1163 ||Epoch_G_Loss:0.1253 ||Epoch_E_Loss:0.1187\n",
            "timer:  0.4294 sec.\n",
            "-------------\n",
            "Epoch 1202/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1202 || Epoch_D_Loss:0.1085 ||Epoch_G_Loss:0.1208 ||Epoch_E_Loss:0.1268\n",
            "timer:  0.4299 sec.\n",
            "-------------\n",
            "Epoch 1203/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1203 || Epoch_D_Loss:0.1000 ||Epoch_G_Loss:0.1180 ||Epoch_E_Loss:0.1313\n",
            "timer:  0.4277 sec.\n",
            "-------------\n",
            "Epoch 1204/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1204 || Epoch_D_Loss:0.0945 ||Epoch_G_Loss:0.1214 ||Epoch_E_Loss:0.1427\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 1205/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1205 || Epoch_D_Loss:0.1015 ||Epoch_G_Loss:0.1251 ||Epoch_E_Loss:0.1435\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 1206/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1206 || Epoch_D_Loss:0.1065 ||Epoch_G_Loss:0.1429 ||Epoch_E_Loss:0.1280\n",
            "timer:  0.4311 sec.\n",
            "-------------\n",
            "Epoch 1207/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1207 || Epoch_D_Loss:0.1048 ||Epoch_G_Loss:0.1183 ||Epoch_E_Loss:0.1360\n",
            "timer:  0.4297 sec.\n",
            "-------------\n",
            "Epoch 1208/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1208 || Epoch_D_Loss:0.1092 ||Epoch_G_Loss:0.1251 ||Epoch_E_Loss:0.1237\n",
            "timer:  0.4312 sec.\n",
            "-------------\n",
            "Epoch 1209/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1209 || Epoch_D_Loss:0.1016 ||Epoch_G_Loss:0.1197 ||Epoch_E_Loss:0.1359\n",
            "timer:  0.4258 sec.\n",
            "-------------\n",
            "Epoch 1210/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1210 || Epoch_D_Loss:0.1010 ||Epoch_G_Loss:0.1334 ||Epoch_E_Loss:0.1349\n",
            "timer:  0.4248 sec.\n",
            "-------------\n",
            "Epoch 1211/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1211 || Epoch_D_Loss:0.0921 ||Epoch_G_Loss:0.1378 ||Epoch_E_Loss:0.1420\n",
            "timer:  0.4277 sec.\n",
            "-------------\n",
            "Epoch 1212/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1212 || Epoch_D_Loss:0.0882 ||Epoch_G_Loss:0.1385 ||Epoch_E_Loss:0.1483\n",
            "timer:  0.4287 sec.\n",
            "-------------\n",
            "Epoch 1213/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1213 || Epoch_D_Loss:0.0999 ||Epoch_G_Loss:0.1444 ||Epoch_E_Loss:0.1293\n",
            "timer:  0.4279 sec.\n",
            "-------------\n",
            "Epoch 1214/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1214 || Epoch_D_Loss:0.1071 ||Epoch_G_Loss:0.1283 ||Epoch_E_Loss:0.1341\n",
            "timer:  0.4293 sec.\n",
            "-------------\n",
            "Epoch 1215/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1215 || Epoch_D_Loss:0.1169 ||Epoch_G_Loss:0.1209 ||Epoch_E_Loss:0.1269\n",
            "timer:  0.4273 sec.\n",
            "-------------\n",
            "Epoch 1216/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1216 || Epoch_D_Loss:0.1129 ||Epoch_G_Loss:0.1333 ||Epoch_E_Loss:0.1265\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 1217/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1217 || Epoch_D_Loss:0.1049 ||Epoch_G_Loss:0.1261 ||Epoch_E_Loss:0.1367\n",
            "timer:  0.4281 sec.\n",
            "-------------\n",
            "Epoch 1218/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1218 || Epoch_D_Loss:0.0964 ||Epoch_G_Loss:0.1198 ||Epoch_E_Loss:0.1399\n",
            "timer:  0.4277 sec.\n",
            "-------------\n",
            "Epoch 1219/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1219 || Epoch_D_Loss:0.0952 ||Epoch_G_Loss:0.1367 ||Epoch_E_Loss:0.1368\n",
            "timer:  0.4307 sec.\n",
            "-------------\n",
            "Epoch 1220/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1220 || Epoch_D_Loss:0.0932 ||Epoch_G_Loss:0.1327 ||Epoch_E_Loss:0.1469\n",
            "timer:  0.4289 sec.\n",
            "-------------\n",
            "Epoch 1221/1500\n",
            "-------------\n",
            "（train）\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "-------------\n",
            "epoch 1221 || Epoch_D_Loss:0.1017 ||Epoch_G_Loss:0.1355 ||Epoch_E_Loss:0.1373\n",
            "timer:  0.4277 sec.\n",
            "-------------\n",
            "Epoch 1222/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1222 || Epoch_D_Loss:0.1021 ||Epoch_G_Loss:0.1415 ||Epoch_E_Loss:0.1424\n",
            "timer:  0.4279 sec.\n",
            "-------------\n",
            "Epoch 1223/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1223 || Epoch_D_Loss:0.0972 ||Epoch_G_Loss:0.1304 ||Epoch_E_Loss:0.1442\n",
            "timer:  0.4251 sec.\n",
            "-------------\n",
            "Epoch 1224/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1224 || Epoch_D_Loss:0.1071 ||Epoch_G_Loss:0.1526 ||Epoch_E_Loss:0.1281\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 1225/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1225 || Epoch_D_Loss:0.1082 ||Epoch_G_Loss:0.1250 ||Epoch_E_Loss:0.1413\n",
            "timer:  0.4277 sec.\n",
            "-------------\n",
            "Epoch 1226/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1226 || Epoch_D_Loss:0.0997 ||Epoch_G_Loss:0.1295 ||Epoch_E_Loss:0.1310\n",
            "timer:  0.4315 sec.\n",
            "-------------\n",
            "Epoch 1227/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1227 || Epoch_D_Loss:0.0999 ||Epoch_G_Loss:0.1115 ||Epoch_E_Loss:0.1402\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 1228/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1228 || Epoch_D_Loss:0.1165 ||Epoch_G_Loss:0.1408 ||Epoch_E_Loss:0.1245\n",
            "timer:  0.4255 sec.\n",
            "-------------\n",
            "Epoch 1229/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1229 || Epoch_D_Loss:0.1062 ||Epoch_G_Loss:0.1319 ||Epoch_E_Loss:0.1321\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 1230/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1230 || Epoch_D_Loss:0.0996 ||Epoch_G_Loss:0.1268 ||Epoch_E_Loss:0.1397\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 1231/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1231 || Epoch_D_Loss:0.0979 ||Epoch_G_Loss:0.1396 ||Epoch_E_Loss:0.1349\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 1232/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1232 || Epoch_D_Loss:0.0898 ||Epoch_G_Loss:0.1393 ||Epoch_E_Loss:0.1449\n",
            "timer:  0.4286 sec.\n",
            "-------------\n",
            "Epoch 1233/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1233 || Epoch_D_Loss:0.0915 ||Epoch_G_Loss:0.1266 ||Epoch_E_Loss:0.1470\n",
            "timer:  0.4287 sec.\n",
            "-------------\n",
            "Epoch 1234/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1234 || Epoch_D_Loss:0.0892 ||Epoch_G_Loss:0.1577 ||Epoch_E_Loss:0.1406\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 1235/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1235 || Epoch_D_Loss:0.0876 ||Epoch_G_Loss:0.1416 ||Epoch_E_Loss:0.1537\n",
            "timer:  0.4253 sec.\n",
            "-------------\n",
            "Epoch 1236/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1236 || Epoch_D_Loss:0.0969 ||Epoch_G_Loss:0.1499 ||Epoch_E_Loss:0.1398\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 1237/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1237 || Epoch_D_Loss:0.0997 ||Epoch_G_Loss:0.1228 ||Epoch_E_Loss:0.1480\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 1238/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1238 || Epoch_D_Loss:0.1083 ||Epoch_G_Loss:0.1389 ||Epoch_E_Loss:0.1248\n",
            "timer:  0.4270 sec.\n",
            "-------------\n",
            "Epoch 1239/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1239 || Epoch_D_Loss:0.1185 ||Epoch_G_Loss:0.1146 ||Epoch_E_Loss:0.1326\n",
            "timer:  0.4306 sec.\n",
            "-------------\n",
            "Epoch 1240/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1240 || Epoch_D_Loss:0.1137 ||Epoch_G_Loss:0.1192 ||Epoch_E_Loss:0.1268\n",
            "timer:  0.4289 sec.\n",
            "-------------\n",
            "Epoch 1241/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1241 || Epoch_D_Loss:0.1156 ||Epoch_G_Loss:0.1266 ||Epoch_E_Loss:0.1221\n",
            "timer:  0.4271 sec.\n",
            "-------------\n",
            "Epoch 1242/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1242 || Epoch_D_Loss:0.0957 ||Epoch_G_Loss:0.1367 ||Epoch_E_Loss:0.1358\n",
            "timer:  0.4244 sec.\n",
            "-------------\n",
            "Epoch 1243/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1243 || Epoch_D_Loss:0.0851 ||Epoch_G_Loss:0.1304 ||Epoch_E_Loss:0.1588\n",
            "timer:  0.4243 sec.\n",
            "-------------\n",
            "Epoch 1244/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1244 || Epoch_D_Loss:0.0806 ||Epoch_G_Loss:0.1529 ||Epoch_E_Loss:0.1552\n",
            "timer:  0.4255 sec.\n",
            "-------------\n",
            "Epoch 1245/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1245 || Epoch_D_Loss:0.0833 ||Epoch_G_Loss:0.1521 ||Epoch_E_Loss:0.1459\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 1246/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1246 || Epoch_D_Loss:0.0888 ||Epoch_G_Loss:0.1286 ||Epoch_E_Loss:0.1616\n",
            "timer:  0.4275 sec.\n",
            "-------------\n",
            "Epoch 1247/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1247 || Epoch_D_Loss:0.1036 ||Epoch_G_Loss:0.1231 ||Epoch_E_Loss:0.1376\n",
            "timer:  0.4295 sec.\n",
            "-------------\n",
            "Epoch 1248/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1248 || Epoch_D_Loss:0.1101 ||Epoch_G_Loss:0.1204 ||Epoch_E_Loss:0.1266\n",
            "timer:  0.4288 sec.\n",
            "-------------\n",
            "Epoch 1249/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1249 || Epoch_D_Loss:0.1210 ||Epoch_G_Loss:0.1166 ||Epoch_E_Loss:0.1205\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 1250/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1250 || Epoch_D_Loss:0.1194 ||Epoch_G_Loss:0.1133 ||Epoch_E_Loss:0.1262\n",
            "timer:  0.4255 sec.\n",
            "-------------\n",
            "Epoch 1251/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1251 || Epoch_D_Loss:0.1031 ||Epoch_G_Loss:0.1231 ||Epoch_E_Loss:0.1324\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 1252/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1252 || Epoch_D_Loss:0.0968 ||Epoch_G_Loss:0.1487 ||Epoch_E_Loss:0.1396\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 1253/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1253 || Epoch_D_Loss:0.0888 ||Epoch_G_Loss:0.1490 ||Epoch_E_Loss:0.1487\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 1254/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1254 || Epoch_D_Loss:0.0835 ||Epoch_G_Loss:0.1524 ||Epoch_E_Loss:0.1542\n",
            "timer:  0.4271 sec.\n",
            "-------------\n",
            "Epoch 1255/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1255 || Epoch_D_Loss:0.0866 ||Epoch_G_Loss:0.1477 ||Epoch_E_Loss:0.1552\n",
            "timer:  0.4277 sec.\n",
            "-------------\n",
            "Epoch 1256/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1256 || Epoch_D_Loss:0.0918 ||Epoch_G_Loss:0.1501 ||Epoch_E_Loss:0.1430\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 1257/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1257 || Epoch_D_Loss:0.0996 ||Epoch_G_Loss:0.1368 ||Epoch_E_Loss:0.1332\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 1258/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1258 || Epoch_D_Loss:0.1022 ||Epoch_G_Loss:0.1311 ||Epoch_E_Loss:0.1339\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 1259/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1259 || Epoch_D_Loss:0.1045 ||Epoch_G_Loss:0.1128 ||Epoch_E_Loss:0.1404\n",
            "timer:  0.4248 sec.\n",
            "-------------\n",
            "Epoch 1260/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1260 || Epoch_D_Loss:0.1056 ||Epoch_G_Loss:0.1373 ||Epoch_E_Loss:0.1284\n",
            "timer:  0.4254 sec.\n",
            "-------------\n",
            "Epoch 1261/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1261 || Epoch_D_Loss:0.1026 ||Epoch_G_Loss:0.1323 ||Epoch_E_Loss:0.1440\n",
            "timer:  0.4278 sec.\n",
            "-------------\n",
            "Epoch 1262/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1262 || Epoch_D_Loss:0.1027 ||Epoch_G_Loss:0.1442 ||Epoch_E_Loss:0.1418\n",
            "timer:  0.4278 sec.\n",
            "-------------\n",
            "Epoch 1263/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1263 || Epoch_D_Loss:0.1006 ||Epoch_G_Loss:0.1341 ||Epoch_E_Loss:0.1483\n",
            "timer:  0.4291 sec.\n",
            "-------------\n",
            "Epoch 1264/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1264 || Epoch_D_Loss:0.1013 ||Epoch_G_Loss:0.1313 ||Epoch_E_Loss:0.1434\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 1265/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1265 || Epoch_D_Loss:0.1124 ||Epoch_G_Loss:0.1204 ||Epoch_E_Loss:0.1551\n",
            "timer:  0.4254 sec.\n",
            "-------------\n",
            "Epoch 1266/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1266 || Epoch_D_Loss:0.1150 ||Epoch_G_Loss:0.1347 ||Epoch_E_Loss:0.1409\n",
            "timer:  0.4255 sec.\n",
            "-------------\n",
            "Epoch 1267/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1267 || Epoch_D_Loss:0.1079 ||Epoch_G_Loss:0.1213 ||Epoch_E_Loss:0.1352\n",
            "timer:  0.4248 sec.\n",
            "-------------\n",
            "Epoch 1268/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1268 || Epoch_D_Loss:0.1138 ||Epoch_G_Loss:0.1179 ||Epoch_E_Loss:0.1366\n",
            "timer:  0.4276 sec.\n",
            "-------------\n",
            "Epoch 1269/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1269 || Epoch_D_Loss:0.1082 ||Epoch_G_Loss:0.1376 ||Epoch_E_Loss:0.1298\n",
            "timer:  0.4271 sec.\n",
            "-------------\n",
            "Epoch 1270/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1270 || Epoch_D_Loss:0.1136 ||Epoch_G_Loss:0.1245 ||Epoch_E_Loss:0.1374\n",
            "timer:  0.4294 sec.\n",
            "-------------\n",
            "Epoch 1271/1500\n",
            "-------------\n",
            "（train）\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "-------------\n",
            "epoch 1271 || Epoch_D_Loss:0.1103 ||Epoch_G_Loss:0.1433 ||Epoch_E_Loss:0.1238\n",
            "timer:  0.4302 sec.\n",
            "-------------\n",
            "Epoch 1272/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1272 || Epoch_D_Loss:0.1089 ||Epoch_G_Loss:0.1192 ||Epoch_E_Loss:0.1442\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 1273/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1273 || Epoch_D_Loss:0.1131 ||Epoch_G_Loss:0.1224 ||Epoch_E_Loss:0.1280\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 1274/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1274 || Epoch_D_Loss:0.1022 ||Epoch_G_Loss:0.1362 ||Epoch_E_Loss:0.1321\n",
            "timer:  0.4245 sec.\n",
            "-------------\n",
            "Epoch 1275/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1275 || Epoch_D_Loss:0.0978 ||Epoch_G_Loss:0.1145 ||Epoch_E_Loss:0.1428\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 1276/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1276 || Epoch_D_Loss:0.0955 ||Epoch_G_Loss:0.1378 ||Epoch_E_Loss:0.1384\n",
            "timer:  0.4284 sec.\n",
            "-------------\n",
            "Epoch 1277/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1277 || Epoch_D_Loss:0.0935 ||Epoch_G_Loss:0.1313 ||Epoch_E_Loss:0.1397\n",
            "timer:  0.4288 sec.\n",
            "-------------\n",
            "Epoch 1278/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1278 || Epoch_D_Loss:0.0914 ||Epoch_G_Loss:0.1333 ||Epoch_E_Loss:0.1451\n",
            "timer:  0.4281 sec.\n",
            "-------------\n",
            "Epoch 1279/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1279 || Epoch_D_Loss:0.0910 ||Epoch_G_Loss:0.1410 ||Epoch_E_Loss:0.1371\n",
            "timer:  0.4289 sec.\n",
            "-------------\n",
            "Epoch 1280/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1280 || Epoch_D_Loss:0.1002 ||Epoch_G_Loss:0.1236 ||Epoch_E_Loss:0.1450\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 1281/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1281 || Epoch_D_Loss:0.1078 ||Epoch_G_Loss:0.1377 ||Epoch_E_Loss:0.1245\n",
            "timer:  0.4250 sec.\n",
            "-------------\n",
            "Epoch 1282/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1282 || Epoch_D_Loss:0.0971 ||Epoch_G_Loss:0.1274 ||Epoch_E_Loss:0.1352\n",
            "timer:  0.4246 sec.\n",
            "-------------\n",
            "Epoch 1283/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1283 || Epoch_D_Loss:0.0900 ||Epoch_G_Loss:0.1357 ||Epoch_E_Loss:0.1425\n",
            "timer:  0.4295 sec.\n",
            "-------------\n",
            "Epoch 1284/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1284 || Epoch_D_Loss:0.0861 ||Epoch_G_Loss:0.1454 ||Epoch_E_Loss:0.1479\n",
            "timer:  0.4288 sec.\n",
            "-------------\n",
            "Epoch 1285/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1285 || Epoch_D_Loss:0.0870 ||Epoch_G_Loss:0.1481 ||Epoch_E_Loss:0.1525\n",
            "timer:  0.4279 sec.\n",
            "-------------\n",
            "Epoch 1286/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1286 || Epoch_D_Loss:0.0877 ||Epoch_G_Loss:0.1383 ||Epoch_E_Loss:0.1519\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 1287/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1287 || Epoch_D_Loss:0.0966 ||Epoch_G_Loss:0.1219 ||Epoch_E_Loss:0.1415\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 1288/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1288 || Epoch_D_Loss:0.1187 ||Epoch_G_Loss:0.1207 ||Epoch_E_Loss:0.1271\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 1289/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1289 || Epoch_D_Loss:0.1106 ||Epoch_G_Loss:0.1286 ||Epoch_E_Loss:0.1301\n",
            "timer:  0.4285 sec.\n",
            "-------------\n",
            "Epoch 1290/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1290 || Epoch_D_Loss:0.0948 ||Epoch_G_Loss:0.1349 ||Epoch_E_Loss:0.1447\n",
            "timer:  0.4287 sec.\n",
            "-------------\n",
            "Epoch 1291/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1291 || Epoch_D_Loss:0.0814 ||Epoch_G_Loss:0.1630 ||Epoch_E_Loss:0.1507\n",
            "timer:  0.4290 sec.\n",
            "-------------\n",
            "Epoch 1292/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1292 || Epoch_D_Loss:0.0781 ||Epoch_G_Loss:0.1779 ||Epoch_E_Loss:0.1630\n",
            "timer:  0.4278 sec.\n",
            "-------------\n",
            "Epoch 1293/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1293 || Epoch_D_Loss:0.0785 ||Epoch_G_Loss:0.1507 ||Epoch_E_Loss:0.1717\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 1294/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1294 || Epoch_D_Loss:0.0985 ||Epoch_G_Loss:0.1355 ||Epoch_E_Loss:0.1464\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 1295/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1295 || Epoch_D_Loss:0.1188 ||Epoch_G_Loss:0.1322 ||Epoch_E_Loss:0.1197\n",
            "timer:  0.4276 sec.\n",
            "-------------\n",
            "Epoch 1296/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1296 || Epoch_D_Loss:0.1252 ||Epoch_G_Loss:0.1180 ||Epoch_E_Loss:0.1233\n",
            "timer:  0.4296 sec.\n",
            "-------------\n",
            "Epoch 1297/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1297 || Epoch_D_Loss:0.1041 ||Epoch_G_Loss:0.1296 ||Epoch_E_Loss:0.1352\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 1298/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1298 || Epoch_D_Loss:0.0852 ||Epoch_G_Loss:0.1383 ||Epoch_E_Loss:0.1520\n",
            "timer:  0.4273 sec.\n",
            "-------------\n",
            "Epoch 1299/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1299 || Epoch_D_Loss:0.0856 ||Epoch_G_Loss:0.1494 ||Epoch_E_Loss:0.1614\n",
            "timer:  0.4284 sec.\n",
            "-------------\n",
            "Epoch 1300/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1300 || Epoch_D_Loss:0.0839 ||Epoch_G_Loss:0.1525 ||Epoch_E_Loss:0.1541\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 1301/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1301 || Epoch_D_Loss:0.0915 ||Epoch_G_Loss:0.1401 ||Epoch_E_Loss:0.1576\n",
            "timer:  0.4254 sec.\n",
            "-------------\n",
            "Epoch 1302/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1302 || Epoch_D_Loss:0.1015 ||Epoch_G_Loss:0.1313 ||Epoch_E_Loss:0.1441\n",
            "timer:  0.4276 sec.\n",
            "-------------\n",
            "Epoch 1303/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1303 || Epoch_D_Loss:0.1087 ||Epoch_G_Loss:0.1274 ||Epoch_E_Loss:0.1435\n",
            "timer:  0.4305 sec.\n",
            "-------------\n",
            "Epoch 1304/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1304 || Epoch_D_Loss:0.1078 ||Epoch_G_Loss:0.1440 ||Epoch_E_Loss:0.1324\n",
            "timer:  0.4293 sec.\n",
            "-------------\n",
            "Epoch 1305/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1305 || Epoch_D_Loss:0.0947 ||Epoch_G_Loss:0.1328 ||Epoch_E_Loss:0.1487\n",
            "timer:  0.4277 sec.\n",
            "-------------\n",
            "Epoch 1306/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1306 || Epoch_D_Loss:0.1026 ||Epoch_G_Loss:0.1408 ||Epoch_E_Loss:0.1424\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 1307/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1307 || Epoch_D_Loss:0.0960 ||Epoch_G_Loss:0.1403 ||Epoch_E_Loss:0.1444\n",
            "timer:  0.4293 sec.\n",
            "-------------\n",
            "Epoch 1308/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1308 || Epoch_D_Loss:0.1017 ||Epoch_G_Loss:0.1299 ||Epoch_E_Loss:0.1612\n",
            "timer:  0.4290 sec.\n",
            "-------------\n",
            "Epoch 1309/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1309 || Epoch_D_Loss:0.0966 ||Epoch_G_Loss:0.1425 ||Epoch_E_Loss:0.1462\n",
            "timer:  0.4324 sec.\n",
            "-------------\n",
            "Epoch 1310/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1310 || Epoch_D_Loss:0.0968 ||Epoch_G_Loss:0.1385 ||Epoch_E_Loss:0.1547\n",
            "timer:  0.4289 sec.\n",
            "-------------\n",
            "Epoch 1311/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1311 || Epoch_D_Loss:0.0936 ||Epoch_G_Loss:0.1567 ||Epoch_E_Loss:0.1445\n",
            "timer:  0.4279 sec.\n",
            "-------------\n",
            "Epoch 1312/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1312 || Epoch_D_Loss:0.0995 ||Epoch_G_Loss:0.1511 ||Epoch_E_Loss:0.1577\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 1313/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1313 || Epoch_D_Loss:0.1024 ||Epoch_G_Loss:0.1543 ||Epoch_E_Loss:0.1418\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 1314/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1314 || Epoch_D_Loss:0.0962 ||Epoch_G_Loss:0.1386 ||Epoch_E_Loss:0.1461\n",
            "timer:  0.4293 sec.\n",
            "-------------\n",
            "Epoch 1315/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1315 || Epoch_D_Loss:0.0963 ||Epoch_G_Loss:0.1465 ||Epoch_E_Loss:0.1675\n",
            "timer:  0.4310 sec.\n",
            "-------------\n",
            "Epoch 1316/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1316 || Epoch_D_Loss:0.0929 ||Epoch_G_Loss:0.1601 ||Epoch_E_Loss:0.1494\n",
            "timer:  0.4294 sec.\n",
            "-------------\n",
            "Epoch 1317/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1317 || Epoch_D_Loss:0.1000 ||Epoch_G_Loss:0.1404 ||Epoch_E_Loss:0.1548\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 1318/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1318 || Epoch_D_Loss:0.1145 ||Epoch_G_Loss:0.1264 ||Epoch_E_Loss:0.1418\n",
            "timer:  0.4248 sec.\n",
            "-------------\n",
            "Epoch 1319/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1319 || Epoch_D_Loss:0.1186 ||Epoch_G_Loss:0.1268 ||Epoch_E_Loss:0.1331\n",
            "timer:  0.4249 sec.\n",
            "-------------\n",
            "Epoch 1320/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1320 || Epoch_D_Loss:0.1114 ||Epoch_G_Loss:0.1295 ||Epoch_E_Loss:0.1495\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 1321/1500\n",
            "-------------\n",
            "（train）\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "-------------\n",
            "epoch 1321 || Epoch_D_Loss:0.0961 ||Epoch_G_Loss:0.1589 ||Epoch_E_Loss:0.1435\n",
            "timer:  0.4284 sec.\n",
            "-------------\n",
            "Epoch 1322/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1322 || Epoch_D_Loss:0.0937 ||Epoch_G_Loss:0.1314 ||Epoch_E_Loss:0.1810\n",
            "timer:  0.4251 sec.\n",
            "-------------\n",
            "Epoch 1323/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1323 || Epoch_D_Loss:0.0946 ||Epoch_G_Loss:0.1585 ||Epoch_E_Loss:0.1552\n",
            "timer:  0.4258 sec.\n",
            "-------------\n",
            "Epoch 1324/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1324 || Epoch_D_Loss:0.0883 ||Epoch_G_Loss:0.1642 ||Epoch_E_Loss:0.1570\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 1325/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1325 || Epoch_D_Loss:0.0969 ||Epoch_G_Loss:0.1444 ||Epoch_E_Loss:0.1519\n",
            "timer:  0.4255 sec.\n",
            "-------------\n",
            "Epoch 1326/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1326 || Epoch_D_Loss:0.1066 ||Epoch_G_Loss:0.1361 ||Epoch_E_Loss:0.1518\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 1327/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1327 || Epoch_D_Loss:0.1121 ||Epoch_G_Loss:0.1298 ||Epoch_E_Loss:0.1410\n",
            "timer:  0.4273 sec.\n",
            "-------------\n",
            "Epoch 1328/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1328 || Epoch_D_Loss:0.1102 ||Epoch_G_Loss:0.1390 ||Epoch_E_Loss:0.1440\n",
            "timer:  0.4270 sec.\n",
            "-------------\n",
            "Epoch 1329/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1329 || Epoch_D_Loss:0.1092 ||Epoch_G_Loss:0.1247 ||Epoch_E_Loss:0.1503\n",
            "timer:  0.4245 sec.\n",
            "-------------\n",
            "Epoch 1330/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1330 || Epoch_D_Loss:0.1156 ||Epoch_G_Loss:0.1489 ||Epoch_E_Loss:0.1449\n",
            "timer:  0.4242 sec.\n",
            "-------------\n",
            "Epoch 1331/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1331 || Epoch_D_Loss:0.1095 ||Epoch_G_Loss:0.1272 ||Epoch_E_Loss:0.1411\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 1332/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1332 || Epoch_D_Loss:0.1301 ||Epoch_G_Loss:0.1358 ||Epoch_E_Loss:0.1380\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 1333/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1333 || Epoch_D_Loss:0.1095 ||Epoch_G_Loss:0.1295 ||Epoch_E_Loss:0.1518\n",
            "timer:  0.4291 sec.\n",
            "-------------\n",
            "Epoch 1334/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1334 || Epoch_D_Loss:0.1169 ||Epoch_G_Loss:0.1176 ||Epoch_E_Loss:0.1416\n",
            "timer:  0.4284 sec.\n",
            "-------------\n",
            "Epoch 1335/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1335 || Epoch_D_Loss:0.1053 ||Epoch_G_Loss:0.1330 ||Epoch_E_Loss:0.1453\n",
            "timer:  0.4273 sec.\n",
            "-------------\n",
            "Epoch 1336/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1336 || Epoch_D_Loss:0.1148 ||Epoch_G_Loss:0.1266 ||Epoch_E_Loss:0.1349\n",
            "timer:  0.4251 sec.\n",
            "-------------\n",
            "Epoch 1337/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1337 || Epoch_D_Loss:0.1275 ||Epoch_G_Loss:0.1210 ||Epoch_E_Loss:0.1185\n",
            "timer:  0.4248 sec.\n",
            "-------------\n",
            "Epoch 1338/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1338 || Epoch_D_Loss:0.1217 ||Epoch_G_Loss:0.1138 ||Epoch_E_Loss:0.1231\n",
            "timer:  0.4252 sec.\n",
            "-------------\n",
            "Epoch 1339/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1339 || Epoch_D_Loss:0.1061 ||Epoch_G_Loss:0.1249 ||Epoch_E_Loss:0.1282\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 1340/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1340 || Epoch_D_Loss:0.0941 ||Epoch_G_Loss:0.1476 ||Epoch_E_Loss:0.1389\n",
            "timer:  0.4280 sec.\n",
            "-------------\n",
            "Epoch 1341/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1341 || Epoch_D_Loss:0.0843 ||Epoch_G_Loss:0.1398 ||Epoch_E_Loss:0.1665\n",
            "timer:  0.4279 sec.\n",
            "-------------\n",
            "Epoch 1342/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1342 || Epoch_D_Loss:0.0806 ||Epoch_G_Loss:0.1626 ||Epoch_E_Loss:0.1532\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 1343/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1343 || Epoch_D_Loss:0.0864 ||Epoch_G_Loss:0.1524 ||Epoch_E_Loss:0.1539\n",
            "timer:  0.4240 sec.\n",
            "-------------\n",
            "Epoch 1344/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1344 || Epoch_D_Loss:0.0964 ||Epoch_G_Loss:0.1235 ||Epoch_E_Loss:0.1484\n",
            "timer:  0.4249 sec.\n",
            "-------------\n",
            "Epoch 1345/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1345 || Epoch_D_Loss:0.1138 ||Epoch_G_Loss:0.1200 ||Epoch_E_Loss:0.1323\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 1346/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1346 || Epoch_D_Loss:0.1021 ||Epoch_G_Loss:0.1259 ||Epoch_E_Loss:0.1345\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 1347/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1347 || Epoch_D_Loss:0.0955 ||Epoch_G_Loss:0.1254 ||Epoch_E_Loss:0.1499\n",
            "timer:  0.4282 sec.\n",
            "-------------\n",
            "Epoch 1348/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1348 || Epoch_D_Loss:0.0872 ||Epoch_G_Loss:0.1553 ||Epoch_E_Loss:0.1457\n",
            "timer:  0.4284 sec.\n",
            "-------------\n",
            "Epoch 1349/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1349 || Epoch_D_Loss:0.0928 ||Epoch_G_Loss:0.1398 ||Epoch_E_Loss:0.1466\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 1350/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1350 || Epoch_D_Loss:0.1064 ||Epoch_G_Loss:0.1334 ||Epoch_E_Loss:0.1447\n",
            "timer:  0.4246 sec.\n",
            "-------------\n",
            "Epoch 1351/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1351 || Epoch_D_Loss:0.0926 ||Epoch_G_Loss:0.1284 ||Epoch_E_Loss:0.1559\n",
            "timer:  0.4253 sec.\n",
            "-------------\n",
            "Epoch 1352/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1352 || Epoch_D_Loss:0.1021 ||Epoch_G_Loss:0.1406 ||Epoch_E_Loss:0.1493\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 1353/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1353 || Epoch_D_Loss:0.1029 ||Epoch_G_Loss:0.1467 ||Epoch_E_Loss:0.1417\n",
            "timer:  0.4275 sec.\n",
            "-------------\n",
            "Epoch 1354/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1354 || Epoch_D_Loss:0.0945 ||Epoch_G_Loss:0.1460 ||Epoch_E_Loss:0.1396\n",
            "timer:  0.4252 sec.\n",
            "-------------\n",
            "Epoch 1355/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1355 || Epoch_D_Loss:0.0949 ||Epoch_G_Loss:0.1377 ||Epoch_E_Loss:0.1457\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 1356/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1356 || Epoch_D_Loss:0.1019 ||Epoch_G_Loss:0.1297 ||Epoch_E_Loss:0.1413\n",
            "timer:  0.4279 sec.\n",
            "-------------\n",
            "Epoch 1357/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1357 || Epoch_D_Loss:0.1004 ||Epoch_G_Loss:0.1225 ||Epoch_E_Loss:0.1456\n",
            "timer:  0.4277 sec.\n",
            "-------------\n",
            "Epoch 1358/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1358 || Epoch_D_Loss:0.0901 ||Epoch_G_Loss:0.1409 ||Epoch_E_Loss:0.1458\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 1359/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1359 || Epoch_D_Loss:0.0903 ||Epoch_G_Loss:0.1406 ||Epoch_E_Loss:0.1559\n",
            "timer:  0.4248 sec.\n",
            "-------------\n",
            "Epoch 1360/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1360 || Epoch_D_Loss:0.0908 ||Epoch_G_Loss:0.1514 ||Epoch_E_Loss:0.1513\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 1361/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1361 || Epoch_D_Loss:0.0870 ||Epoch_G_Loss:0.1488 ||Epoch_E_Loss:0.1503\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 1362/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1362 || Epoch_D_Loss:0.0988 ||Epoch_G_Loss:0.1272 ||Epoch_E_Loss:0.1482\n",
            "timer:  0.4270 sec.\n",
            "-------------\n",
            "Epoch 1363/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1363 || Epoch_D_Loss:0.1098 ||Epoch_G_Loss:0.1409 ||Epoch_E_Loss:0.1323\n",
            "timer:  0.4255 sec.\n",
            "-------------\n",
            "Epoch 1364/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1364 || Epoch_D_Loss:0.0984 ||Epoch_G_Loss:0.1410 ||Epoch_E_Loss:0.1354\n",
            "timer:  0.4278 sec.\n",
            "-------------\n",
            "Epoch 1365/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1365 || Epoch_D_Loss:0.0984 ||Epoch_G_Loss:0.1314 ||Epoch_E_Loss:0.1476\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 1366/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1366 || Epoch_D_Loss:0.0909 ||Epoch_G_Loss:0.1477 ||Epoch_E_Loss:0.1453\n",
            "timer:  0.4273 sec.\n",
            "-------------\n",
            "Epoch 1367/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1367 || Epoch_D_Loss:0.0862 ||Epoch_G_Loss:0.1477 ||Epoch_E_Loss:0.1533\n",
            "timer:  0.4258 sec.\n",
            "-------------\n",
            "Epoch 1368/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1368 || Epoch_D_Loss:0.0848 ||Epoch_G_Loss:0.1451 ||Epoch_E_Loss:0.1539\n",
            "timer:  0.4252 sec.\n",
            "-------------\n",
            "Epoch 1369/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1369 || Epoch_D_Loss:0.0938 ||Epoch_G_Loss:0.1304 ||Epoch_E_Loss:0.1459\n",
            "timer:  0.4244 sec.\n",
            "-------------\n",
            "Epoch 1370/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1370 || Epoch_D_Loss:0.0972 ||Epoch_G_Loss:0.1414 ||Epoch_E_Loss:0.1429\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 1371/1500\n",
            "-------------\n",
            "（train）\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "-------------\n",
            "epoch 1371 || Epoch_D_Loss:0.0957 ||Epoch_G_Loss:0.1441 ||Epoch_E_Loss:0.1475\n",
            "timer:  0.4296 sec.\n",
            "-------------\n",
            "Epoch 1372/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1372 || Epoch_D_Loss:0.0999 ||Epoch_G_Loss:0.1438 ||Epoch_E_Loss:0.1657\n",
            "timer:  0.4281 sec.\n",
            "-------------\n",
            "Epoch 1373/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1373 || Epoch_D_Loss:0.0938 ||Epoch_G_Loss:0.1469 ||Epoch_E_Loss:0.1466\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 1374/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1374 || Epoch_D_Loss:0.0976 ||Epoch_G_Loss:0.1234 ||Epoch_E_Loss:0.1512\n",
            "timer:  0.4234 sec.\n",
            "-------------\n",
            "Epoch 1375/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1375 || Epoch_D_Loss:0.0979 ||Epoch_G_Loss:0.1295 ||Epoch_E_Loss:0.1450\n",
            "timer:  0.4243 sec.\n",
            "-------------\n",
            "Epoch 1376/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1376 || Epoch_D_Loss:0.0908 ||Epoch_G_Loss:0.1403 ||Epoch_E_Loss:0.1419\n",
            "timer:  0.4250 sec.\n",
            "-------------\n",
            "Epoch 1377/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1377 || Epoch_D_Loss:0.0949 ||Epoch_G_Loss:0.1365 ||Epoch_E_Loss:0.1448\n",
            "timer:  0.4300 sec.\n",
            "-------------\n",
            "Epoch 1378/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1378 || Epoch_D_Loss:0.0943 ||Epoch_G_Loss:0.1329 ||Epoch_E_Loss:0.1473\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 1379/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1379 || Epoch_D_Loss:0.1019 ||Epoch_G_Loss:0.1303 ||Epoch_E_Loss:0.1391\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 1380/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1380 || Epoch_D_Loss:0.0955 ||Epoch_G_Loss:0.1353 ||Epoch_E_Loss:0.1478\n",
            "timer:  0.4244 sec.\n",
            "-------------\n",
            "Epoch 1381/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1381 || Epoch_D_Loss:0.0952 ||Epoch_G_Loss:0.1511 ||Epoch_E_Loss:0.1470\n",
            "timer:  0.4242 sec.\n",
            "-------------\n",
            "Epoch 1382/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1382 || Epoch_D_Loss:0.0915 ||Epoch_G_Loss:0.1337 ||Epoch_E_Loss:0.1505\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 1383/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1383 || Epoch_D_Loss:0.0919 ||Epoch_G_Loss:0.1363 ||Epoch_E_Loss:0.1474\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 1384/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1384 || Epoch_D_Loss:0.0943 ||Epoch_G_Loss:0.1450 ||Epoch_E_Loss:0.1418\n",
            "timer:  0.4284 sec.\n",
            "-------------\n",
            "Epoch 1385/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1385 || Epoch_D_Loss:0.0959 ||Epoch_G_Loss:0.1400 ||Epoch_E_Loss:0.1463\n",
            "timer:  0.4276 sec.\n",
            "-------------\n",
            "Epoch 1386/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1386 || Epoch_D_Loss:0.0896 ||Epoch_G_Loss:0.1369 ||Epoch_E_Loss:0.1479\n",
            "timer:  0.4279 sec.\n",
            "-------------\n",
            "Epoch 1387/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1387 || Epoch_D_Loss:0.0924 ||Epoch_G_Loss:0.1360 ||Epoch_E_Loss:0.1527\n",
            "timer:  0.4251 sec.\n",
            "-------------\n",
            "Epoch 1388/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1388 || Epoch_D_Loss:0.0963 ||Epoch_G_Loss:0.1334 ||Epoch_E_Loss:0.1491\n",
            "timer:  0.4237 sec.\n",
            "-------------\n",
            "Epoch 1389/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1389 || Epoch_D_Loss:0.1089 ||Epoch_G_Loss:0.1393 ||Epoch_E_Loss:0.1456\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 1390/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1390 || Epoch_D_Loss:0.0944 ||Epoch_G_Loss:0.1527 ||Epoch_E_Loss:0.1435\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 1391/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1391 || Epoch_D_Loss:0.0855 ||Epoch_G_Loss:0.1454 ||Epoch_E_Loss:0.1548\n",
            "timer:  0.4252 sec.\n",
            "-------------\n",
            "Epoch 1392/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1392 || Epoch_D_Loss:0.0823 ||Epoch_G_Loss:0.1469 ||Epoch_E_Loss:0.1566\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 1393/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1393 || Epoch_D_Loss:0.0880 ||Epoch_G_Loss:0.1387 ||Epoch_E_Loss:0.1571\n",
            "timer:  0.4258 sec.\n",
            "-------------\n",
            "Epoch 1394/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1394 || Epoch_D_Loss:0.1047 ||Epoch_G_Loss:0.1310 ||Epoch_E_Loss:0.1383\n",
            "timer:  0.4273 sec.\n",
            "-------------\n",
            "Epoch 1395/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1395 || Epoch_D_Loss:0.0980 ||Epoch_G_Loss:0.1459 ||Epoch_E_Loss:0.1432\n",
            "timer:  0.4284 sec.\n",
            "-------------\n",
            "Epoch 1396/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1396 || Epoch_D_Loss:0.0945 ||Epoch_G_Loss:0.1508 ||Epoch_E_Loss:0.1404\n",
            "timer:  0.4277 sec.\n",
            "-------------\n",
            "Epoch 1397/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1397 || Epoch_D_Loss:0.0936 ||Epoch_G_Loss:0.1289 ||Epoch_E_Loss:0.1652\n",
            "timer:  0.4252 sec.\n",
            "-------------\n",
            "Epoch 1398/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1398 || Epoch_D_Loss:0.0961 ||Epoch_G_Loss:0.1394 ||Epoch_E_Loss:0.1463\n",
            "timer:  0.4234 sec.\n",
            "-------------\n",
            "Epoch 1399/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1399 || Epoch_D_Loss:0.0963 ||Epoch_G_Loss:0.1485 ||Epoch_E_Loss:0.1479\n",
            "timer:  0.4232 sec.\n",
            "-------------\n",
            "Epoch 1400/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1400 || Epoch_D_Loss:0.0856 ||Epoch_G_Loss:0.1473 ||Epoch_E_Loss:0.1609\n",
            "timer:  0.4260 sec.\n",
            "-------------\n",
            "Epoch 1401/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1401 || Epoch_D_Loss:0.0859 ||Epoch_G_Loss:0.1514 ||Epoch_E_Loss:0.1575\n",
            "timer:  0.4301 sec.\n",
            "-------------\n",
            "Epoch 1402/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1402 || Epoch_D_Loss:0.0897 ||Epoch_G_Loss:0.1438 ||Epoch_E_Loss:0.1512\n",
            "timer:  0.4273 sec.\n",
            "-------------\n",
            "Epoch 1403/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1403 || Epoch_D_Loss:0.0979 ||Epoch_G_Loss:0.1484 ||Epoch_E_Loss:0.1390\n",
            "timer:  0.4310 sec.\n",
            "-------------\n",
            "Epoch 1404/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1404 || Epoch_D_Loss:0.1054 ||Epoch_G_Loss:0.1280 ||Epoch_E_Loss:0.1567\n",
            "timer:  0.4362 sec.\n",
            "-------------\n",
            "Epoch 1405/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1405 || Epoch_D_Loss:0.0983 ||Epoch_G_Loss:0.1559 ||Epoch_E_Loss:0.1428\n",
            "timer:  0.4258 sec.\n",
            "-------------\n",
            "Epoch 1406/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1406 || Epoch_D_Loss:0.0850 ||Epoch_G_Loss:0.1580 ||Epoch_E_Loss:0.1559\n",
            "timer:  0.4360 sec.\n",
            "-------------\n",
            "Epoch 1407/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1407 || Epoch_D_Loss:0.0833 ||Epoch_G_Loss:0.1514 ||Epoch_E_Loss:0.1738\n",
            "timer:  0.4458 sec.\n",
            "-------------\n",
            "Epoch 1408/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1408 || Epoch_D_Loss:0.0816 ||Epoch_G_Loss:0.1597 ||Epoch_E_Loss:0.1547\n",
            "timer:  0.4286 sec.\n",
            "-------------\n",
            "Epoch 1409/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1409 || Epoch_D_Loss:0.0912 ||Epoch_G_Loss:0.1482 ||Epoch_E_Loss:0.1522\n",
            "timer:  0.4256 sec.\n",
            "-------------\n",
            "Epoch 1410/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1410 || Epoch_D_Loss:0.1042 ||Epoch_G_Loss:0.1407 ||Epoch_E_Loss:0.1347\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 1411/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1411 || Epoch_D_Loss:0.1085 ||Epoch_G_Loss:0.1345 ||Epoch_E_Loss:0.1381\n",
            "timer:  0.4244 sec.\n",
            "-------------\n",
            "Epoch 1412/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1412 || Epoch_D_Loss:0.0971 ||Epoch_G_Loss:0.1309 ||Epoch_E_Loss:0.1465\n",
            "timer:  0.4246 sec.\n",
            "-------------\n",
            "Epoch 1413/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1413 || Epoch_D_Loss:0.0901 ||Epoch_G_Loss:0.1341 ||Epoch_E_Loss:0.1595\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 1414/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1414 || Epoch_D_Loss:0.0851 ||Epoch_G_Loss:0.1483 ||Epoch_E_Loss:0.1547\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 1415/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1415 || Epoch_D_Loss:0.0835 ||Epoch_G_Loss:0.1588 ||Epoch_E_Loss:0.1585\n",
            "timer:  0.4257 sec.\n",
            "-------------\n",
            "Epoch 1416/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1416 || Epoch_D_Loss:0.0796 ||Epoch_G_Loss:0.1513 ||Epoch_E_Loss:0.1675\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 1417/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1417 || Epoch_D_Loss:0.0845 ||Epoch_G_Loss:0.1461 ||Epoch_E_Loss:0.1555\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 1418/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1418 || Epoch_D_Loss:0.0930 ||Epoch_G_Loss:0.1356 ||Epoch_E_Loss:0.1525\n",
            "timer:  0.4248 sec.\n",
            "-------------\n",
            "Epoch 1419/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1419 || Epoch_D_Loss:0.1048 ||Epoch_G_Loss:0.1324 ||Epoch_E_Loss:0.1469\n",
            "timer:  0.4284 sec.\n",
            "-------------\n",
            "Epoch 1420/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1420 || Epoch_D_Loss:0.1043 ||Epoch_G_Loss:0.1447 ||Epoch_E_Loss:0.1428\n",
            "timer:  0.4279 sec.\n",
            "-------------\n",
            "Epoch 1421/1500\n",
            "-------------\n",
            "（train）\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "-------------\n",
            "epoch 1421 || Epoch_D_Loss:0.0918 ||Epoch_G_Loss:0.1483 ||Epoch_E_Loss:0.1504\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 1422/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1422 || Epoch_D_Loss:0.0836 ||Epoch_G_Loss:0.1508 ||Epoch_E_Loss:0.1612\n",
            "timer:  0.4271 sec.\n",
            "-------------\n",
            "Epoch 1423/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1423 || Epoch_D_Loss:0.0829 ||Epoch_G_Loss:0.1531 ||Epoch_E_Loss:0.1608\n",
            "timer:  0.4251 sec.\n",
            "-------------\n",
            "Epoch 1424/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1424 || Epoch_D_Loss:0.0886 ||Epoch_G_Loss:0.1576 ||Epoch_E_Loss:0.1522\n",
            "timer:  0.4255 sec.\n",
            "-------------\n",
            "Epoch 1425/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1425 || Epoch_D_Loss:0.0976 ||Epoch_G_Loss:0.1541 ||Epoch_E_Loss:0.1504\n",
            "timer:  0.4246 sec.\n",
            "-------------\n",
            "Epoch 1426/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1426 || Epoch_D_Loss:0.0957 ||Epoch_G_Loss:0.1408 ||Epoch_E_Loss:0.1422\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 1427/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1427 || Epoch_D_Loss:0.0937 ||Epoch_G_Loss:0.1379 ||Epoch_E_Loss:0.1625\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 1428/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1428 || Epoch_D_Loss:0.0848 ||Epoch_G_Loss:0.1727 ||Epoch_E_Loss:0.1546\n",
            "timer:  0.4246 sec.\n",
            "-------------\n",
            "Epoch 1429/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1429 || Epoch_D_Loss:0.0814 ||Epoch_G_Loss:0.1491 ||Epoch_E_Loss:0.1639\n",
            "timer:  0.4250 sec.\n",
            "-------------\n",
            "Epoch 1430/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1430 || Epoch_D_Loss:0.0844 ||Epoch_G_Loss:0.1468 ||Epoch_E_Loss:0.1630\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 1431/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1431 || Epoch_D_Loss:0.0904 ||Epoch_G_Loss:0.1448 ||Epoch_E_Loss:0.1512\n",
            "timer:  0.4277 sec.\n",
            "-------------\n",
            "Epoch 1432/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1432 || Epoch_D_Loss:0.1011 ||Epoch_G_Loss:0.1548 ||Epoch_E_Loss:0.1426\n",
            "timer:  0.4276 sec.\n",
            "-------------\n",
            "Epoch 1433/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1433 || Epoch_D_Loss:0.0948 ||Epoch_G_Loss:0.1379 ||Epoch_E_Loss:0.1625\n",
            "timer:  0.4279 sec.\n",
            "-------------\n",
            "Epoch 1434/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1434 || Epoch_D_Loss:0.1013 ||Epoch_G_Loss:0.1614 ||Epoch_E_Loss:0.1515\n",
            "timer:  0.4281 sec.\n",
            "-------------\n",
            "Epoch 1435/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1435 || Epoch_D_Loss:0.0832 ||Epoch_G_Loss:0.1496 ||Epoch_E_Loss:0.1632\n",
            "timer:  0.4249 sec.\n",
            "-------------\n",
            "Epoch 1436/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1436 || Epoch_D_Loss:0.0878 ||Epoch_G_Loss:0.1469 ||Epoch_E_Loss:0.1690\n",
            "timer:  0.4239 sec.\n",
            "-------------\n",
            "Epoch 1437/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1437 || Epoch_D_Loss:0.0854 ||Epoch_G_Loss:0.1691 ||Epoch_E_Loss:0.1649\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 1438/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1438 || Epoch_D_Loss:0.0897 ||Epoch_G_Loss:0.1561 ||Epoch_E_Loss:0.1567\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 1439/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1439 || Epoch_D_Loss:0.0874 ||Epoch_G_Loss:0.1526 ||Epoch_E_Loss:0.1557\n",
            "timer:  0.4250 sec.\n",
            "-------------\n",
            "Epoch 1440/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1440 || Epoch_D_Loss:0.0960 ||Epoch_G_Loss:0.1493 ||Epoch_E_Loss:0.1445\n",
            "timer:  0.4271 sec.\n",
            "-------------\n",
            "Epoch 1441/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1441 || Epoch_D_Loss:0.0984 ||Epoch_G_Loss:0.1420 ||Epoch_E_Loss:0.1564\n",
            "timer:  0.4253 sec.\n",
            "-------------\n",
            "Epoch 1442/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1442 || Epoch_D_Loss:0.1022 ||Epoch_G_Loss:0.1412 ||Epoch_E_Loss:0.1592\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 1443/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1443 || Epoch_D_Loss:0.0864 ||Epoch_G_Loss:0.1602 ||Epoch_E_Loss:0.1567\n",
            "timer:  0.4272 sec.\n",
            "-------------\n",
            "Epoch 1444/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1444 || Epoch_D_Loss:0.0783 ||Epoch_G_Loss:0.1558 ||Epoch_E_Loss:0.1735\n",
            "timer:  0.4304 sec.\n",
            "-------------\n",
            "Epoch 1445/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1445 || Epoch_D_Loss:0.0954 ||Epoch_G_Loss:0.1422 ||Epoch_E_Loss:0.1710\n",
            "timer:  0.4299 sec.\n",
            "-------------\n",
            "Epoch 1446/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1446 || Epoch_D_Loss:0.0998 ||Epoch_G_Loss:0.1438 ||Epoch_E_Loss:0.1531\n",
            "timer:  0.4273 sec.\n",
            "-------------\n",
            "Epoch 1447/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1447 || Epoch_D_Loss:0.1107 ||Epoch_G_Loss:0.1389 ||Epoch_E_Loss:0.1471\n",
            "timer:  0.4276 sec.\n",
            "-------------\n",
            "Epoch 1448/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1448 || Epoch_D_Loss:0.1023 ||Epoch_G_Loss:0.1505 ||Epoch_E_Loss:0.1448\n",
            "timer:  0.4262 sec.\n",
            "-------------\n",
            "Epoch 1449/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1449 || Epoch_D_Loss:0.0964 ||Epoch_G_Loss:0.1414 ||Epoch_E_Loss:0.1540\n",
            "timer:  0.4294 sec.\n",
            "-------------\n",
            "Epoch 1450/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1450 || Epoch_D_Loss:0.0834 ||Epoch_G_Loss:0.1475 ||Epoch_E_Loss:0.1642\n",
            "timer:  0.4283 sec.\n",
            "-------------\n",
            "Epoch 1451/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1451 || Epoch_D_Loss:0.0827 ||Epoch_G_Loss:0.1771 ||Epoch_E_Loss:0.1651\n",
            "timer:  0.4273 sec.\n",
            "-------------\n",
            "Epoch 1452/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1452 || Epoch_D_Loss:0.0865 ||Epoch_G_Loss:0.1542 ||Epoch_E_Loss:0.1563\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 1453/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1453 || Epoch_D_Loss:0.0963 ||Epoch_G_Loss:0.1461 ||Epoch_E_Loss:0.1468\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 1454/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1454 || Epoch_D_Loss:0.1001 ||Epoch_G_Loss:0.1556 ||Epoch_E_Loss:0.1509\n",
            "timer:  0.4274 sec.\n",
            "-------------\n",
            "Epoch 1455/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1455 || Epoch_D_Loss:0.0936 ||Epoch_G_Loss:0.1580 ||Epoch_E_Loss:0.1541\n",
            "timer:  0.4308 sec.\n",
            "-------------\n",
            "Epoch 1456/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1456 || Epoch_D_Loss:0.0983 ||Epoch_G_Loss:0.1578 ||Epoch_E_Loss:0.1698\n",
            "timer:  0.4282 sec.\n",
            "-------------\n",
            "Epoch 1457/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1457 || Epoch_D_Loss:0.0827 ||Epoch_G_Loss:0.1667 ||Epoch_E_Loss:0.1630\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 1458/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1458 || Epoch_D_Loss:0.0765 ||Epoch_G_Loss:0.1567 ||Epoch_E_Loss:0.1659\n",
            "timer:  0.4275 sec.\n",
            "-------------\n",
            "Epoch 1459/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1459 || Epoch_D_Loss:0.0845 ||Epoch_G_Loss:0.1490 ||Epoch_E_Loss:0.1606\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 1460/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1460 || Epoch_D_Loss:0.0860 ||Epoch_G_Loss:0.1451 ||Epoch_E_Loss:0.1614\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 1461/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1461 || Epoch_D_Loss:0.0892 ||Epoch_G_Loss:0.1502 ||Epoch_E_Loss:0.1569\n",
            "timer:  0.4289 sec.\n",
            "-------------\n",
            "Epoch 1462/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1462 || Epoch_D_Loss:0.0909 ||Epoch_G_Loss:0.1467 ||Epoch_E_Loss:0.1515\n",
            "timer:  0.4283 sec.\n",
            "-------------\n",
            "Epoch 1463/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1463 || Epoch_D_Loss:0.0884 ||Epoch_G_Loss:0.1437 ||Epoch_E_Loss:0.1548\n",
            "timer:  0.4269 sec.\n",
            "-------------\n",
            "Epoch 1464/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1464 || Epoch_D_Loss:0.0946 ||Epoch_G_Loss:0.1389 ||Epoch_E_Loss:0.1557\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 1465/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1465 || Epoch_D_Loss:0.0960 ||Epoch_G_Loss:0.1610 ||Epoch_E_Loss:0.1463\n",
            "timer:  0.4275 sec.\n",
            "-------------\n",
            "Epoch 1466/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1466 || Epoch_D_Loss:0.0826 ||Epoch_G_Loss:0.1524 ||Epoch_E_Loss:0.1693\n",
            "timer:  0.4285 sec.\n",
            "-------------\n",
            "Epoch 1467/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1467 || Epoch_D_Loss:0.0822 ||Epoch_G_Loss:0.1744 ||Epoch_E_Loss:0.1610\n",
            "timer:  0.4289 sec.\n",
            "-------------\n",
            "Epoch 1468/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1468 || Epoch_D_Loss:0.0836 ||Epoch_G_Loss:0.1505 ||Epoch_E_Loss:0.1655\n",
            "timer:  0.4277 sec.\n",
            "-------------\n",
            "Epoch 1469/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1469 || Epoch_D_Loss:0.0841 ||Epoch_G_Loss:0.1687 ||Epoch_E_Loss:0.1500\n",
            "timer:  0.4279 sec.\n",
            "-------------\n",
            "Epoch 1470/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1470 || Epoch_D_Loss:0.0890 ||Epoch_G_Loss:0.1489 ||Epoch_E_Loss:0.1573\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 1471/1500\n",
            "-------------\n",
            "（train）\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "-------------\n",
            "epoch 1471 || Epoch_D_Loss:0.0852 ||Epoch_G_Loss:0.1446 ||Epoch_E_Loss:0.1625\n",
            "timer:  0.4275 sec.\n",
            "-------------\n",
            "Epoch 1472/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1472 || Epoch_D_Loss:0.1007 ||Epoch_G_Loss:0.1458 ||Epoch_E_Loss:0.1533\n",
            "timer:  0.4290 sec.\n",
            "-------------\n",
            "Epoch 1473/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1473 || Epoch_D_Loss:0.0916 ||Epoch_G_Loss:0.1582 ||Epoch_E_Loss:0.1524\n",
            "timer:  0.4283 sec.\n",
            "-------------\n",
            "Epoch 1474/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1474 || Epoch_D_Loss:0.0918 ||Epoch_G_Loss:0.1530 ||Epoch_E_Loss:0.1595\n",
            "timer:  0.4266 sec.\n",
            "-------------\n",
            "Epoch 1475/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1475 || Epoch_D_Loss:0.0892 ||Epoch_G_Loss:0.1563 ||Epoch_E_Loss:0.1588\n",
            "timer:  0.4263 sec.\n",
            "-------------\n",
            "Epoch 1476/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1476 || Epoch_D_Loss:0.0924 ||Epoch_G_Loss:0.1479 ||Epoch_E_Loss:0.1591\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 1477/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1477 || Epoch_D_Loss:0.0916 ||Epoch_G_Loss:0.1452 ||Epoch_E_Loss:0.1498\n",
            "timer:  0.4248 sec.\n",
            "-------------\n",
            "Epoch 1478/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1478 || Epoch_D_Loss:0.0887 ||Epoch_G_Loss:0.1353 ||Epoch_E_Loss:0.1589\n",
            "timer:  0.4282 sec.\n",
            "-------------\n",
            "Epoch 1479/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1479 || Epoch_D_Loss:0.0926 ||Epoch_G_Loss:0.1564 ||Epoch_E_Loss:0.1558\n",
            "timer:  0.4265 sec.\n",
            "-------------\n",
            "Epoch 1480/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1480 || Epoch_D_Loss:0.0922 ||Epoch_G_Loss:0.1566 ||Epoch_E_Loss:0.1555\n",
            "timer:  0.4267 sec.\n",
            "-------------\n",
            "Epoch 1481/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1481 || Epoch_D_Loss:0.0967 ||Epoch_G_Loss:0.1529 ||Epoch_E_Loss:0.1568\n",
            "timer:  0.4298 sec.\n",
            "-------------\n",
            "Epoch 1482/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1482 || Epoch_D_Loss:0.0985 ||Epoch_G_Loss:0.1388 ||Epoch_E_Loss:0.1569\n",
            "timer:  0.4259 sec.\n",
            "-------------\n",
            "Epoch 1483/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1483 || Epoch_D_Loss:0.1046 ||Epoch_G_Loss:0.1413 ||Epoch_E_Loss:0.1457\n",
            "timer:  0.4253 sec.\n",
            "-------------\n",
            "Epoch 1484/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1484 || Epoch_D_Loss:0.0909 ||Epoch_G_Loss:0.1552 ||Epoch_E_Loss:0.1553\n",
            "timer:  0.4261 sec.\n",
            "-------------\n",
            "Epoch 1485/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1485 || Epoch_D_Loss:0.0781 ||Epoch_G_Loss:0.1790 ||Epoch_E_Loss:0.1629\n",
            "timer:  0.4250 sec.\n",
            "-------------\n",
            "Epoch 1486/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1486 || Epoch_D_Loss:0.0718 ||Epoch_G_Loss:0.1746 ||Epoch_E_Loss:0.1733\n",
            "timer:  0.4286 sec.\n",
            "-------------\n",
            "Epoch 1487/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1487 || Epoch_D_Loss:0.0781 ||Epoch_G_Loss:0.1640 ||Epoch_E_Loss:0.1641\n",
            "timer:  0.4278 sec.\n",
            "-------------\n",
            "Epoch 1488/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1488 || Epoch_D_Loss:0.0972 ||Epoch_G_Loss:0.1593 ||Epoch_E_Loss:0.1482\n",
            "timer:  0.4254 sec.\n",
            "-------------\n",
            "Epoch 1489/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1489 || Epoch_D_Loss:0.0952 ||Epoch_G_Loss:0.1404 ||Epoch_E_Loss:0.1450\n",
            "timer:  0.4253 sec.\n",
            "-------------\n",
            "Epoch 1490/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1490 || Epoch_D_Loss:0.0914 ||Epoch_G_Loss:0.1458 ||Epoch_E_Loss:0.1510\n",
            "timer:  0.4254 sec.\n",
            "-------------\n",
            "Epoch 1491/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1491 || Epoch_D_Loss:0.0820 ||Epoch_G_Loss:0.1399 ||Epoch_E_Loss:0.1625\n",
            "timer:  0.4268 sec.\n",
            "-------------\n",
            "Epoch 1492/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1492 || Epoch_D_Loss:0.0763 ||Epoch_G_Loss:0.1543 ||Epoch_E_Loss:0.1688\n",
            "timer:  0.4294 sec.\n",
            "-------------\n",
            "Epoch 1493/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1493 || Epoch_D_Loss:0.0917 ||Epoch_G_Loss:0.1496 ||Epoch_E_Loss:0.1571\n",
            "timer:  0.4276 sec.\n",
            "-------------\n",
            "Epoch 1494/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1494 || Epoch_D_Loss:0.0848 ||Epoch_G_Loss:0.1653 ||Epoch_E_Loss:0.1530\n",
            "timer:  0.4253 sec.\n",
            "-------------\n",
            "Epoch 1495/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1495 || Epoch_D_Loss:0.0887 ||Epoch_G_Loss:0.1563 ||Epoch_E_Loss:0.1557\n",
            "timer:  0.4246 sec.\n",
            "-------------\n",
            "Epoch 1496/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1496 || Epoch_D_Loss:0.0873 ||Epoch_G_Loss:0.1532 ||Epoch_E_Loss:0.1560\n",
            "timer:  0.4243 sec.\n",
            "-------------\n",
            "Epoch 1497/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1497 || Epoch_D_Loss:0.0833 ||Epoch_G_Loss:0.1528 ||Epoch_E_Loss:0.1605\n",
            "timer:  0.4264 sec.\n",
            "-------------\n",
            "Epoch 1498/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1498 || Epoch_D_Loss:0.0914 ||Epoch_G_Loss:0.1592 ||Epoch_E_Loss:0.1607\n",
            "timer:  0.4301 sec.\n",
            "-------------\n",
            "Epoch 1499/1500\n",
            "-------------\n",
            "（train）\n",
            "-------------\n",
            "epoch 1499 || Epoch_D_Loss:0.0864 ||Epoch_G_Loss:0.1538 ||Epoch_E_Loss:0.1576\n",
            "timer:  0.4286 sec.\n",
            "総イタレーション回数: 10501\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IL_pxhfEL8K5",
        "colab_type": "code",
        "colab": {},
        "outputId": "c2240826-639b-44b5-9457-51f0f9f18cc5"
      },
      "source": [
        "# 生成画像と訓練データを可視化する\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# 入力の乱数生成\n",
        "batch_size = 8\n",
        "z_dim = 20\n",
        "fixed_z = torch.randn(batch_size, z_dim)\n",
        "G_update.eval()\n",
        "fake_images = G_update(fixed_z.to(device))\n",
        "\n",
        "# 訓練データ\n",
        "batch_iterator = iter(train_dataloader)  # イテレータに変換\n",
        "imges = next(batch_iterator)  # 1番目の要素を取り出す\n",
        "\n",
        "\n",
        "# 出力\n",
        "fig = plt.figure(figsize=(15, 6))\n",
        "for i in range(0, 5):\n",
        "    # 上段に訓練データを\n",
        "    plt.subplot(2, 5, i+1)\n",
        "    plt.imshow(imges[i][0].cpu().detach().numpy(), 'gray')\n",
        "\n",
        "    # 下段に生成データを表示する\n",
        "    plt.subplot(2, 5, 5+i+1)\n",
        "    plt.imshow(fake_images[i][0].cpu().detach().numpy(), 'gray')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZKa9G-CZL8K7",
        "colab_type": "text"
      },
      "source": [
        "# テスト画像で異常検知する"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GcB7w-4xL8K8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# テスト用のDataLoaderの作成\n",
        "\n",
        "\n",
        "def make_test_datapath_list():\n",
        "    \"\"\"学習、検証の画像データとアノテーションデータへのファイルパスリストを作成する。 \"\"\"\n",
        "\n",
        "    train_img_list = list()  # 画像ファイルパスを格納\n",
        "\n",
        "    for img_idx in range(5):\n",
        "        img_path = \"./data/test_28size/img_7_\" + str(img_idx)+'.jpg'\n",
        "        train_img_list.append(img_path)\n",
        "\n",
        "        img_path = \"./data/test_28size/img_8_\" + str(img_idx)+'.jpg'\n",
        "        train_img_list.append(img_path)\n",
        "\n",
        "        img_path = \"./data/test_28size/img_2_\" + str(img_idx)+'.jpg'\n",
        "        train_img_list.append(img_path)\n",
        "\n",
        "    return train_img_list\n",
        "\n",
        "\n",
        "# ファイルリストを作成\n",
        "test_img_list = make_test_datapath_list()\n",
        "\n",
        "# Datasetを作成\n",
        "mean = (0.5,)\n",
        "std = (0.5,)\n",
        "test_dataset = GAN_Img_Dataset(\n",
        "    file_list=test_img_list, transform=ImageTransform(mean, std))\n",
        "\n",
        "# DataLoaderを作成\n",
        "batch_size = 5\n",
        "\n",
        "test_dataloader = torch.utils.data.DataLoader(\n",
        "    test_dataset, batch_size=batch_size, shuffle=False)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ua6uY7_L8K-",
        "colab_type": "code",
        "colab": {},
        "outputId": "0fed73c2-783f-4e59-cd77-7b65bbf27930"
      },
      "source": [
        "# テストデータの確認\n",
        "batch_iterator = iter(test_dataloader)  # イテレータに変換\n",
        "imges = next(batch_iterator)  # 1番目の要素を取り出す\n",
        "\n",
        "fig = plt.figure(figsize=(15, 6))\n",
        "for i in range(0, 5):\n",
        "    # 上段に訓練データを\n",
        "    plt.subplot(2, 5, i+1)\n",
        "    plt.imshow(imges[i][0].cpu().detach().numpy(), 'gray')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OB-bso3WL8LB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def Anomaly_score(x, fake_img, z_out_real, D, Lambda=0.1):\n",
        "\n",
        "    # テスト画像xと生成画像fake_imgのピクセルレベルの差の絶対値を求めて、ミニバッチごとに和を求める\n",
        "    residual_loss = torch.abs(x-fake_img)\n",
        "    residual_loss = residual_loss.view(residual_loss.size()[0], -1)\n",
        "    residual_loss = torch.sum(residual_loss, dim=1)\n",
        "\n",
        "    # テスト画像xと生成画像fake_imgを識別器Dに入力し、特徴量マップを取り出す\n",
        "\n",
        "    _, x_feature = D(x, z_out_real)\n",
        "    _, G_feature = D(fake_img, z_out_real)\n",
        "\n",
        "    # テスト画像xと生成画像fake_imgの特徴量の差の絶対値を求めて、ミニバッチごとに和を求める\n",
        "    discrimination_loss = torch.abs(x_feature-G_feature)\n",
        "    discrimination_loss = discrimination_loss.view(\n",
        "        discrimination_loss.size()[0], -1)\n",
        "    discrimination_loss = torch.sum(discrimination_loss, dim=1)\n",
        "\n",
        "    # ミニバッチごとに2種類の損失を足し算する\n",
        "    loss_each = (1-Lambda)*residual_loss + Lambda*discrimination_loss\n",
        "\n",
        "    # ミニバッチ全部の損失を求める\n",
        "    total_loss = torch.sum(loss_each)\n",
        "\n",
        "    return total_loss, loss_each, residual_loss\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wl7OER2yL8LD",
        "colab_type": "code",
        "colab": {},
        "outputId": "94ba7339-b73c-4b7e-d52e-e1dcb31909fb"
      },
      "source": [
        "# 異常検知したい画像\n",
        "x = imges[0:5]\n",
        "x = x.to(device)\n",
        "\n",
        "# 教師データの画像をエンコードしてzにしてから、Gで生成\n",
        "E_update.eval()\n",
        "G_update.eval()\n",
        "z_out_real = E_update(imges.to(device))\n",
        "imges_reconstract = G_update(z_out_real)\n",
        "\n",
        "# 損失を求める\n",
        "loss, loss_each, residual_loss_each = Anomaly_score(\n",
        "    x, imges_reconstract, z_out_real, D_update, Lambda=0.1)\n",
        "\n",
        "# 損失の計算。トータルの損失\n",
        "loss_each = loss_each.cpu().detach().numpy()\n",
        "print(\"total loss：\", np.round(loss_each, 0))\n",
        "\n",
        "# 画像を可視化\n",
        "fig = plt.figure(figsize=(15, 6))\n",
        "for i in range(0, 5):\n",
        "    # 上段に訓練データを\n",
        "    plt.subplot(2, 5, i+1)\n",
        "    plt.imshow(imges[i][0].cpu().detach().numpy(), 'gray')\n",
        "\n",
        "    # 下段に生成データを表示する\n",
        "    plt.subplot(2, 5, 5+i+1)\n",
        "    plt.imshow(imges_reconstract[i][0].cpu().detach().numpy(), 'gray')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total loss： [171. 205. 285. 190. 161.]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OPOfqd1VL8LE",
        "colab_type": "text"
      },
      "source": [
        "以上"
      ]
    }
  ]
}